Cello

cello

Authors:
(1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu};
(2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu};
(3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro
Related work
Dataset
Experiments
Conclusions and References II. RELATED WORK The University of Rochester Multi-Modal Music Performance Dataset (URMP) [1] is a dataset with 44 Multi-instrument video recordings of classical music pieces. Each instrument present in a piece was recorded separately, both with video and high-quality audio with a stand-alone microphone, in order to have ground-truth individual tracks. Although playing separately, the instruments were coordinated by using a conducting video with a pianist playing in order to set the common timing for the different players. After synchronization, the audio of the individual videos was replaced by the high-quality audio of the microphone and then different recordings were assembled to create the mixture: the individual high-quality audio recordings were added up to create the audio mixture and the visual content was composited in a single video with a common background where all players were arranged at the same level from left to right. For each piece, the dataset provides the musical score in MIDI format, the high-quality individual instrument audio recordings and the videos of the assembled pieces. The instruments present in the dataset, shown in Figure 1, are common instruments in chamber orchestras. In spite of all its good characteristics, it is a small dataset and thus not appropriate for training deep learning architectures. Two other datasets of audio-visual recordings of musical instruments performances have been presented recently: Music [23] and MusicES [31]. Music consists of 536 recordings of solos and 149 videos of duets across 11 categories: accordion, acoustic guitar, cello, clarinet, erhu, flute, saxophone, trumpet, tuba, violin and xylophone. This dataset was gathered by querying YouTube. MusicES [31] is an extension of MUSIC to around the triple of its original size with approximately 1475 recordings but spread in 9 categories instead: accordion, guitar, cello, flute, saxophone, trumpet, tuba, violin and xylophone. There are 7 common categories in MUSIC and Solos: violin, cello, flute, clarinet, saxophone, trumpet and tuba. The common categories between MusicES and Solos are 6 (the former ones except clarinet). Solos and MusicES are complementary. There is only an small intersection of 5% between both, which means both datasets can be combined into a bigger one. We can find in the literature several examples which show the utility of audio-visual datasets. The Sound of Pixels [23] performs audio source separation generating audio spectral components which are further smartly selected by using visual features coming from the video stream to obtain separated sources. This idea was further extended in [20] in order to separate the different sounds present in the mixture in a recursive way. At each stage, the system separates the most salient source from the ones remaining in the mixture. The Sound of Motions [19] uses dense trajectories obtained from optical flow to condition audio source separation, being able even to separate same-instrument mixtures. Visual conditioning is also used in [18] to separate different instruments; during training, a classification loss is used on the separated sounds to enforce object consistency and a co-separation loss forces the estimated individual sounds to produce the original mixtures once reassembled. In [17], the authors developed an energy-based method which minimizes a Non-Negative Matrix Factorization term with an activation matrix which is forced to be aligned to a matrix containing per-source motion information. This motion matrix contains the average magnitude velocities of the clustered motion trajectories in each player bounding box. Recent works show the rising use of skeletons in audiovisual tasks. In Audio to body dynamics [29] authors show it is possible to predict skeletons reproducing the movements of players playing instruments such as piano or violin. Skeletons have proven to be useful for establishing audio-visual correspondences, such as body or finger motion with note onsets or pitch fluctuations, in chamber music performances [21]. A recent work [32] tackles the source separation problem in a similar to Sound of Motions [19] but replacing the dense trajectories by skeleton information. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Authors: Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro Related work Dataset Experiments Conclusions and References Abstract and Intro Abstract and Intro Related work Related work Dataset Dataset Experiments Experiments Conclusions and References Conclusions and References II. RELATED WORK The University of Rochester Multi-Modal Music Performance Dataset (URMP) [1] is a dataset with 44 Multi-instrument video recordings of classical music pieces. Each instrument present in a piece was recorded separately, both with video and high-quality audio with a stand-alone microphone, in order to have ground-truth individual tracks. Although playing separately, the instruments were coordinated by using a conducting video with a pianist playing in order to set the common timing for the different players. After synchronization, the audio of the individual videos was replaced by the high-quality audio of the microphone and then different recordings were assembled to create the mixture: the individual high-quality audio recordings were added up to create the audio mixture and the visual content was composited in a single video with a common background where all players were arranged at the same level from left to right. For each piece, the dataset provides the musical score in MIDI format, the high-quality individual instrument audio recordings and the videos of the assembled pieces. The instruments present in the dataset, shown in Figure 1, are common instruments in chamber orchestras. In spite of all its good characteristics, it is a small dataset and thus not appropriate for training deep learning architectures. Two other datasets of audio-visual recordings of musical instruments performances have been presented recently: Music [23] and MusicES [31]. Music consists of 536 recordings of solos and 149 videos of duets across 11 categories: accordion, acoustic guitar, cello, clarinet, erhu, flute, saxophone, trumpet, tuba, violin and xylophone. This dataset was gathered by querying YouTube. MusicES [31] is an extension of MUSIC to around the triple of its original size with approximately 1475 recordings but spread in 9 categories instead: accordion, guitar, cello, flute, saxophone, trumpet, tuba, violin and xylophone. There are 7 common categories in MUSIC and Solos: violin, cello, flute, clarinet, saxophone, trumpet and tuba. The common categories between MusicES and Solos are 6 (the former ones except clarinet). Solos and MusicES are complementary. There is only an small intersection of 5% between both, which means both datasets can be combined into a bigger one. We can find in the literature several examples which show the utility of audio-visual datasets. The Sound of Pixels [23] performs audio source separation generating audio spectral components which are further smartly selected by using visual features coming from the video stream to obtain separated sources. This idea was further extended in [20] in order to separate the different sounds present in the mixture in a recursive way. At each stage, the system separates the most salient source from the ones remaining in the mixture. The Sound of Motions [19] uses dense trajectories obtained from optical flow to condition audio source separation, being able even to separate same-instrument mixtures. Visual conditioning is also used in [18] to separate different instruments; during training, a classification loss is used on the separated sounds to enforce object consistency and a co-separation loss forces the estimated individual sounds to produce the original mixtures once reassembled. In [17], the authors developed an energy-based method which minimizes a Non-Negative Matrix Factorization term with an activation matrix which is forced to be aligned to a matrix containing per-source motion information. This motion matrix contains the average magnitude velocities of the clustered motion trajectories in each player bounding box. Recent works show the rising use of skeletons in audiovisual tasks. In Audio to body dynamics [29] authors show it is possible to predict skeletons reproducing the movements of players playing instruments such as piano or violin. Skeletons have proven to be useful for establishing audio-visual correspondences, such as body or finger motion with note onsets or pitch fluctuations, in chamber music performances [21]. A recent work [32] tackles the source separation problem in a similar to Sound of Motions [19] but replacing the dense trajectories by skeleton information. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Solos: A Dataset for Audio-Visual Music Analysis - Related work

একক: অডিও-ভিজ্যুয়াল সঙ্গীত বিশ্লেষণের জন্য একটি ডেটাসেট - সম্পর্কিত কাজ

Solos: Ein Datensatz für audiovisuelle Musikanalyse - Verwandte Arbeiten

Solos: un conjunto de datos para el análisis de música audiovisual - Trabajo relacionado

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Travaux connexes

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट - संबंधित कार्य

Solos: オーディオビジュアル音楽分析のためのデータセット - 関連研究

Solos: 시청각 음악 분석을 위한 데이터세트 - 관련 작업

Solos: um conjunto de dados para análise musical audiovisual - Trabalhos relacionados

Соло: набор данных для аудиовизуального музыкального анализа — соответствующая работа

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - İlgili çalışma

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn - Công việc liên quan

Solos：视听音乐分析数据集 - 相关工作

Picture this: two identical coffee shops at opposite corners of a quaint little town. The coffee beans? Sourced from the same Colombian farms. The espresso machines? Same exact models. The ambiance? Practically indistinguishable. Yet, one is thriving, and the other is barely scraping by. So, what sets them apart? Imagine walking into the thriving coffee shop. Mr. Anderson, a regular, walks in right behind you. The barista doesn't just know his order—a tall latte with a hint of vanilla—but also that he prefers almond milk, only on Tuesdays, and that his dog Max had surgery last week. After a quick chat, Mr. Anderson walks out, not just with his latte but also with a complimentary dog treat for Max. That's personalization, and it's precisely what SaaS companies must aim for. We are in a new era of customer relationships—the era of personalization. All the Features Look the Same… Features became commonplace, even mundane. They get replicated at a faster and more efficient rate than ever imagined. The very essence of software became democratized, leading to a paradox: the better the functionalities became, the less they seemed to matter. Consider Zoom. With a vast product offering tailored for different customer segments—businesses, individuals, students, and senior IT leaders— Do we think that Zoom will continue leading the market by providing better virtual meeting features? Here we are, drowning in features that look the same. What if the future of SaaS isn't in what the software can do, but instead in how well it knows what you need it to do? Consider the project management tool that recognizes when you're assigning too many tasks in the late hours of Friday evenings. What if, instead of bombarding you with a generic prompt or a 'how-to' guide, it suggests a more efficient delegation strategy for your next week, allowing you to enjoy a well-deserved weekend? The Crucial Nuances Once a company scales beyond the horizon of product-market fit, it starts noticing the subtleties among its customers. -   Some prefer receiving only critical updates, while others bask in the glory of every new feature. -   Some want to explore a product quietly before speaking with a salesperson, while others wish for immediate human interaction. -   Some expect different values from different product lines. -   Support? It's a whole new Pandora’s box. A user might want extensive documentation, while another requires one-on-one hand-holding. Thus, to maximize growth, it becomes clear that personalizing every single step of the customer journey is not just a luxury—it's a necessity. The Instagram Paradigm Instagram and YouTube have aced personalization in content delivery. Your Instagram feed is fundamentally different from your spouse's, offering a tailored universe. But can this paradigm be replicated when it comes to Marketing, Sales, Product, and Support in SaaS? The Path to Personalization and Revenue Growth So, how does one achieve this level of personalization when there are thousands of Mr. Andersons? At Coho AI, we've distilled it into a three-part symphony: First Movement: Data Consolidation and Normalization Firstly, gather all customer data into a robust, layered data model. -   Layer 1: Meta-data like job title, email, etc., often sourced from third parties. -   Layer 2: Relationship details like product tier, internal communication, sitting in your CRM, or user database. -   Layer 3: Product usage patterns, which require meticulous data collection and translation of raw UI, backend events, and user state. Second Movement: Auto-Segmentation and Actionable Insights Once data is consolidated and normalized, it's time for auto-segmentation. This process continuously evolves, refactoring new data points and allowing faster tailoring of customer experiences. Analyzing segmentations will allow us to learn based on the context of the user, identify leading indicators for desired outcomes, and construct actionable insights on every single user to achieve our goals. This is where insight turns into strategy. Third Movement: Real-Time Automation and Engagement Finally, let's bring in the cellos and violins—emails, pop-ups, feature flags, and a fully integrated subscription management system—all orchestrated in real-time, aimed at engaging the customer when it counts the most. The Standing Ovation Imagine a world where every interaction you have with a SaaS company feels like Mr. Andersons's experience at his favorite coffee shop. That's the kind of responsive, deeply personalized experience that not only fosters loyalty but also turns every user into an advocate. We are past the age of features. The new frontier lies in mastering the symphony of personalization, where every note is tailored to resonate with you—be it in customer support, product experience, or sales interaction. Undertaking this may seem overwhelming at first, but it genuinely doesn't have to be. The real challenge? Making the commitment to this shift and aspiring for your company to be the thriving coffee shop everyone raves about. As for the intricate details and next steps? Coho AI has got you covered, managing the heavy lifting with finesse.

Hello Mr. Anderson: Orchestrating Personalization in the World of SaaS

Head and Shoulders

The Offshore Pirate

Saboteur of Space

How to Make a Cost-effective Flexible Robotic Solution for Low-volume Production

Why Design a Scalable Factory? A “conventional automotive” production line has a requirement to produce volumes in the hundreds of thousands per annum which results in an industry average cycle time requirement of 60 seconds, resulting in a product from the production line every minute. This means that all assembly steps of a certain product must be completed within the cycle time. In turn, this usually leads to breaking up the production process into “stations” with one or more production steps performed at each. The number of steps per station is limited by the cycle time and the duration of the individual step. The system is designed so that each station is sufficiently utilized while still achieving the cycle time. If there is an assembly step that can not be completed in the required cycle time, the stations must be duplicated and run in parallel to achieve the target production output. Time constraint is not the only limitation for the number of steps that can be performed per station. In some cases, the complexity (process or equipment) of a specific step in the assembly process may result in the station performing only the assembly step even though the processing time is well below the cycle time. Problems appear when we are asked to provide a lower-volume production system (longer cycle time per product) or a system that can scale up and down by stages or on demand, or, even worse, a system that can produce various products simultaneously and remain efficient. It means such a production system has to be capable of adapting to different cycle times and different sequences (number, types of operations) of production processes. To meet the demands of “low” volume, easily scalable automated production of complex assemblies with low CapEx and in a small footprint factory, we, the Robofacturin team in Arrival UK, had to come up with a new flexible, scalable architecture, which allows us to process a high number of assembly steps using different joining technologies. Mathematically, in the limit, such a system must consist of universal resources (cells) in many-to-many relationships (matrix). On the one side, each such cell (resource) must be capable of performing all (the more, the better) types of operations. On the other side,  every product for assembling (the input to the system) must consist of the same (the fewer different types, the better) parts or granules (pixels). Of course, this is the description of an ideal, unattainable system. We can only try to make a real system closer to the ideal. But it is impossible for traditional factories, in principle, by design - there is a linear sequence of operations and cells, most of which are unique, etc. We found that the best concept from the other industry is a Datacenter that consists of unified blade servers and which can process different types of load (inputs) balancing between resources (servers). This concept requires: Universal multi-task cell (blade server for our "Datacenter") Flexible transportation system (databus for "Datacenter") Design of products maximum closer to the ideal pixel structure (data for our "Datacenter") The is the subject of this article. We will mention the to explain basic principles, but this topic, along with the one, is the subject of separate articles. It is worth mentioning that the third topic, in practice, means finding a design that is most suitable/compatible with such kind of factory. We can call it Design-to-Robofacturing. first point second third Technology Cell (“blade server”) To fulfil the scalable factory production requirements, the production system must be able to perform many diverse operations in one station to assemble a product. These operations can vary from part pick and place operations to applications of adhesives, sealants, and different types of fasteners. A high number of unique parts that the assembled product consists of also present a requirement for the station in the production system to be able to accommodate a large number of tools. In response to these requirements, we designed a robotic cell that accommodates multiple joining technologies and can process any assembly step of a produced product. We named it a Technology Cell or Smart Cell. A is a product and process-agnostic robotic cell that can be used for different processes and products without changing its Core Blueprint. The is a blueprint of the cell that does not include any equipment that would be specific to a certain product or process. Technology cell Core Blueprint For a Technology Cell to be capable of performing the operations required to assemble a product, a Core Blueprint must be equipped with technologies which give the Technology Cell ability to perform assembly operations for a particular product . At this point, the cell becomes technology specific (eg. bolting, gluing) but remains product agnostic. This means it can assemble any product that fits its working volume and is compatible with joining technologies available in the cell. (blue and green objects in the 2nd image below) A Technology cell can also be equipped with another layer of equipment which can be more product or process focused. This additional layer does not hinder the original capabilities of the technology cell, but it extends them to more unique processes that are only needed in specific steps. An example of such a cell would be the Marriage and Glazing features, where a system for the marriage of drivetrain systems and battery pack with the vehicle is permanently located in the cell . (purple objects in the 3rd image above) Creating a Chipset: Building blocks, Applications and Tooling A flexible factory must be capable of dealing with many different products, systems and specific requirements for production: vehicles (from a small car up to a bus), composite panels, small electronic components, battery packs, etc. The nature of the products can differ in many ways: size, complexity (part count, system count), manufacturing method, production volumes, and special environmental requirements. A scalable factory should not only be able to be deployed rapidly but also react to requirement changes quickly. This means that the design process of the factory should be streamlined as much as possible while still allowing for creativity and innovation. To achieve this, we created a modular method for the design of production systems. The method defines requirements for the design of components that form a manufacturing system (eg. robotic cell). As these components have different functionality, they are split into three categories: Building blocks, Applications and Tooling. Building blocks are components that form the lowest of the manufacturing systems and are technology agnostic. They don't have any value-added manufacturing abilities but provide a framework to which we can apply different technologies to give a system process capability. This framework is called a “Core blueprint” and is the first layer of the solution to which the process capability is added. Typical members of this group are all safety-related, geo pallet lifters, geo fixture positioning systems, and robot assemblies. The image shows a Core Configuration of the Production system consisting of Building Blocks only. At this point, the cell is able to accept the layer of applications. Applications are technologies that give a production system process capability. Examples of such applications are adhesive applications, bolting, picking and placing, trimming, handling, machining, scanning, etc. These applications are product-independent, meaning they don't have any features that are specifically designed to accommodate a unique product. The image shows an (applications) layer being added to the Core blueprint of a production system. At this point, the system becomes . At this point, the cell can accept the Tooling layover. Application technology-oriented is a group of components that form a production system that is unique to a specific product. Examples of such components are part grippers, fixtures, product transport pallets, and moulds. As they are an extension of building blocks and applications, they must share the same design architecture that allows us seamless integration. The tooling blocks can be split into two major categories. The mobile product tooling, which flows through the production via autonomous mobile robots and fixed tooling, can be permanently located in a production system to extend its capabilities for a specific process (eg. pallet exchange).  It’s at this point that the production system has a full production capability. Tooling Each Building block, Application or Tooling has everything required to be “plugged in” to a production system from a hardware and software point of view. At the hardware level, we can imagine this as a standardized connector with power and communication terminals that allow a building block, application or tooling to be seamlessly plugged into a production system. The architect of the software level allows the control system to recognize and control each of these groups via the manufacturing execution system. Integrating a building block, application, or tool can be seen as a standard socket and plug connection. The modularity approach sets a design standard that ensures every element can be plugged into any system design. This allows us to quickly configure any production solution by reusing the objects already in our library and assembling them in a new configuration. As visible from the schematics configurations below, there is a large number of shared objects between very different manufacturing systems (robotic cells). If there is a requirement for a significant number of less complex assemblies to be produced in the factory, it may be sensible to create a few simpler, lower CAPEX and footprint cells with architecture better suited to these types of assemblies. Where a manufactured product is more complex, we can configure a system that will be better suited for the complex use case. Transportation System: Autonomous Mobile Robotics The scalable factory operates with a small footprint and a small number of assembly stations that can process a high number of assembly operations. Therefore we had to come up with a method to be able to feed each of these stations with a large number of tools and parts. In a more conventional production system where each station only performs a small number of operations and uses a small number of tools (grippers or joining equipment), these can be permanently located in the station (robot cell) within reach of the robot that handles it. The reach of the robots is in some cases, enhanced using an external linear axis. However, this still does not satisfy business requirements for the number of tools that must be processed in one station (robot cell)  to meet the requirements of the factory. A monolithic production line only has a small number of steps performed at each station. Therefore the number of tools for each station is relatively low. With fewer assembly stations, significantly more tools must be fed to each station. To meet this requirement, our Mobile robotics team developed an autonomous mobile robot called (wheeled mobility) that can deliver any tool to any assembly station.  The robot does not require any modifications to the facility in which it operates. WeMo uses lidars and cameras for navigating through the facility and precise positioning at the locations where its payload has to be precisely dropped off, picked up, or it’s plugging itself into a charging station. WeMo has an integrated lifting capability which allows it to pick up and drop off objects where required. They can operate as an autonomous, independent unit or can connect into “clusters” and work synchronously to move larger and heavier objects when required. WeMo To achieve the ultimate flexibility, we located the tools away from the cells in the logistics area where the operators load the parts into the robot tooling. When the part is required at the assembly station, the manufacturing execution system orders a mobile robot to collect the tool or a part from the logistics area and deliver it to the robot at a precise time without interfering or slowing down the production process. In the case of the Technology cell, the Tools (T) can be delivered to the 4 outboard locations where they can be picked up by a robot. The image above shows only one logistics area where all the tools are located. However, this doesn’t have to be the case. With the use of autonomous robots for the execution of the logistics system, we can arrange the area where tools are located in as many locations as we like. This allows us to optimize the layout for logistics efficiency, environmental requirements of the parts (batteries), and their need for lift assistance. The AMRs are not only tasked with the tool and part delivery to and from the build stations, but also with moving the assembled product between them. The ability of AMRs to autonomously navigate through the facility allows us to be flexible with the positioning of the assembly stations and arrange them in a configuration that meets the factory production requirements. The image shows a cluster of four technology cells that can be used as a production system with two parallel processes with two assembly stations (cell) each or a single four-station production system. Scalable factories are deployed in existing facilities. Therefore, we have to be able to react to any configuration (shape) of the building. With the use of AMRs,  we have more flexibility to utilise the available floor space. The image above shows the cluster of four Technology cells configured in a four station production process. Technology cells are positioned in a configuration that suits the shape of the facility (black outline). Another benefit of not having assembly stations connected via a rigid transport system (eg. conveyor) is that we can be flexible with our production plan. We can feed different products to the same production stations in any order. The image below shows three different routes through the same production system. The black line makes use of all four stations, the green line only utilizes one assembly station, and the blue line utilizes two assembly stations. This allows us to produce different products in the same production line in parallel without any changes being made to the assembly stations. If one cell becomes un-operational, we can reroute the production to a different cell and still operate without stoppage, but at reduced throughput. This is how our Bicseter smart, scalable factory operates, where we produce subassemblies and the main vehicle assembly using the same Technology cells. Software Creation and Deployment Software that runs the factory assembly process is generated autonomously by different internally developed software packages that streamline the process of generating an assembly sequence code that drives each automated action of the production system. The generation process starts with the generation of the Assembly sequence, which is generated from the mBOM of the product. The sequence is then processed by a motion planner and scheduler, which generates all robot trajectories as a sequential flow of operations. The final piece of the puzzle is the Cell operating system which orchestrates all events that are happening in the production process and collects data on the status and quality of the process. The sequence code generation process starts in the Assembly sequence generator software package. To automatically generate an assembly sequence, an mBOM of a manufactured product is uploaded to the generator. Based on the components of the mBOM, the Assembly sequence generator assigns all logistics and assembly operations to each component in the mBOM. Based on the joint/fastener type that is part of the mBOM, the generator assigns the correct joining method and resource to each step in the assembly. The outputted assembly sequence code is then enriched with information about resources that will be tasked with the execution of the assembly process (resources: Cell, robot, Screwdriver, AMR, Gripper…). The final output of the Assembly process generator is a BoP (bill of the process) MetaGraph. The image above shows a section of the BoP MetaGraph. Each step of the process includes information about the process type and all resources that are part of the processing step. At this point, we don't have any actual process code but rather an instruction that the next software package can interpret in the generation process. The generation of BoP in today's industry is still a manual process, usually done by process engineers in a spreadsheet format with supporting visuals for the PLC and Robotics engineers. As with any manual processes, this is prone to mistakes by engineers as it is very labor-intensive. The scheduler and motion planner package have been developed to read the instructions generated by the Assembly sequence generator and, based on its instructions, generate the robot trajectories based on the virtual twin of the resources that participate in a given assembly step (cell, robot, gripper, part, adhesive model). Any CV (computer vision) algorithms are also added to the package at this point. The instructions package with this additional layer is then handed over to the next system, CellOS. The (CellOS) is the system that orchestrates all actions in the production system. The generated Sequential Function Chart and Robot trajectories are processed in the Flow processor and executed. All sequence control logistic is executed via soft PLC. The only system running locally at the production system is a Safety logic of the production stations which the local safety PLC controls to meet the machine safety requirements. cell operating system The assembly sequence is not hard-coded into a local  PLC but it’s rather streamed through the soft PLC, which gives us the flexibility to change the order of operations, build stations or update process parameters without redeploying PLC code. Each building block, application, and tool also have its virtual twin so the software deployment scenarios can be validated before they are deployed. The software of each building block, application or tool is developed alongside their hardware to allow for seamless integration into the system. Therefore each building block and application, and the tool has its associated software block, which allows the Cell operating system to control them (trigger any actions), collect messages, faults, and telemetric data, as well as deploy any process parameters (eg. torque, stroke, angle). This unlocks the ability to be flexible and agile with the implementation of any process or parameter changes. The schematics show the process from the product mBOM, which is being processed in an Assembly sequence generator into a BoP, enriched with motion and process parameters and deployed to CellOS. Once a scenario (assembly process) is deployed to CellOS, there are no further deployments needed unless there is a need for an update of the process. CellOS talks to each element of a production system (Roboti cell) and cell safety individually. Summary As a result, by following the same evolution that transformed a mainframe into a PC, we can significantly decrease the complexity of the architecture of an assembling factory and achieve new characteristics without losing efficiency resulting in: scalability product agnostic smaller footprint simpler design less CapEx But the most important benefit is that new architecture, by design, allows us to implement and use the power of Artificial intelligence software. This can make our factory smart.

Architecture Design of a Smart, Scalable, AI-driven Robotic Production Factory

Free as in Freedom, by Sam Williams, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . A BRIEF JOURNEY THROUGH HACKER HELL The lead image of this article was generated via HackerNoon's AI Stable Diffusion model using the prompt 'a fiery hell filled with computers'. A BRIEF JOURNEY THROUGH HACKER HELL Richard Stallman stares, unblinking, through the windshield of a rental car, waiting for the light to change as we make our way through downtown Kihei. The two of us are headed to the nearby town of Pa'ia, where we are scheduled to meet up with some software programmers and their wives for dinner in about an hour or so. It's about two hours after Stallman's speech at the Maui High Performance Center, and Kihei, a town that seemed so inviting before the speech, now seems profoundly uncooperative. Like most beach cities, Kihei is a one-dimensional exercise in suburban sprawl. Driving down its main drag, with its endless succession of burger stands, realty agencies, and bikini shops, it's hard not to feel like a steel-coated morsel passing through the alimentary canal of a giant commercial tapeworm. The feeling is exacerbated by the lack of side roads. With nowhere to go but forward, traffic moves in spring-like lurches. 200 yards ahead, a light turns green. By the time we are moving, the light is yellow again. For Stallman, a lifetime resident of the east coast, the prospect of spending the better part of a sunny Hawaiian afternoon trapped in slow traffic is enough to trigger an embolism. Even worse is the knowledge that, with just a few quick right turns a quarter mile back, this whole situation easily could have been avoided. Unfortunately, we are at the mercy of the driver ahead of us, a programmer from the lab who knows the way and who has decided to take us to Pa'ia via the scenic route instead of via the nearby Pilani Highway. "This is terrible," says Stallman between frustrated sighs. "Why didn't we take the other route?" Again, the light a quarter mile ahead of us turns green. Again, we creep forward a few more car lengths. This process continues for another 10 minutes, until we finally reach a major crossroad promising access to the adjacent highway. The driver ahead of us ignores it and continues through the intersection. "Why isn't he turning?" moans Stallman, throwing up his hands in frustration. "Can you believe this?" I decide not to answer either. I find the fact that I am sitting in a car with Stallman in the driver seat, in Maui no less, unbelievable enough. Until two hours ago, I didn't even know Stallman knew how to drive. Now, listening to Yo-Yo Ma's cello playing the mournful bass notes of "Appalachian Journey" on the car stereo and watching the sunset pass by on our left, I do my best to fade into the upholstery. When the next opportunity to turn finally comes up, Stallman hits his right turn signal in an attempt to cue the driver ahead of us. No such luck. Once again, we creep slowly through the intersection, coming to a stop a good 200 yards before the next light. By now, Stallman is livid. "It's like he's deliberately ignoring us," he says, gesturing and pantomiming like an air craft carrier landing-signals officer in a futile attempt to catch our guide's eye. The guide appears unfazed, and for the next five minutes all we see is a small portion of his head in the rearview mirror. I look out Stallman's window. Nearby Kahoolawe and Lanai Islands provide an ideal frame for the setting sun. It's a breathtaking view, the kind that makes moments like this a bit more bearable if you're a Hawaiian native, I suppose. I try to direct Stallman's attention to it, but Stallman, by now obsessed by the inattentiveness of the driver ahead of us, blows me off. When the driver passes through another green light, completely ignoring a "Pilani Highway Next Right," I grit my teeth. I remember an early warning relayed to me by BSD programmer Keith Bostic. "Stallman does not suffer fools gladly," Bostic warned me. "If somebody says or does something stupid, he'll look them in the eye and say, `That's stupid.'" Looking at the oblivious driver ahead of us, I realize that it's the stupidity, not the inconvenience, that's killing Stallman right now. "It's as if he picked this route with absolutely no thought on how to get there efficiently," Stallman says. The word "efficiently" hangs in the air like a bad odor. Few things irritate the hacker mind more than inefficiency. It was the inefficiency of checking the Xerox laser printer two or three times a day that triggered Stallman's initial inquiry into the printer source code. It was the inefficiency of rewriting software tools hijacked by commercial software vendors that led Stallman to battle Symbolics and to launch the GNU Project. If, as Jean Paul Sartre once opined, hell is other people, hacker hell is duplicating other people's stupid mistakes, and it's no exaggeration to say that Stallman's entire life has been an attempt to save mankind from these fiery depths. This hell metaphor becomes all the more apparent as we take in the slowly passing scenery. With its multitude of shops, parking lots, and poorly timed street lights, Kihei seems less like a city and more like a poorly designed software program writ large. Instead of rerouting traffic and distributing vehicles through side streets and expressways, city planners have elected to run everything through a single main drag. From a hacker perspective, sitting in a car amidst all this mess is like listening to a CD rendition of nails on a chalkboard at full volume. "Imperfect systems infuriate hackers," observes Steven Levy, another warning I should have listened to before climbing into the car with Stallman. "This is one reason why hackers generally hate driving cars-the system of randomly programmed red lights and oddly laid out one-way streets causes delays which are so goddamn unnecessary [Levy's emphasis] that the impulse is to rearrange signs, open up traffic-light control boxes . . . redesign the entire system."See Steven Levy, Hackers (Penguin USA [paperback], 1984): 40. More frustrating, however, is the duplicity of our trusted guide. Instead of searching out a clever shortcut-as any true hacker would do on instinct-the driver ahead of us has instead chosen to play along with the city planners' game. Like Virgil in Dante's Inferno, our guide is determined to give us the full guided tour of this hacker hell whether we want it or not. Before I can make this observation to Stallman, the driver finally hits his right turn signal. Stallman's hunched shoulders relax slightly, and for a moment the air of tension within the car dissipates. The tension comes back, however, as the driver in front of us slows down. "Construction Ahead" signs line both sides of the street, and even though the Pilani Highway lies less than a quarter mile off in the distance, the two-lane road between us and the highway is blocked by a dormant bulldozer and two large mounds of dirt. It takes Stallman a few seconds to register what's going on as our guide begins executing a clumsy five-point U-turn in front of us. When he catches a glimpse of the bulldozer and the "No Through Access" signs just beyond, Stallman finally boils over. "Why, why, why?" he whines, throwing his head back. "You should have known the road was blocked. You should have known this way wouldn't work. You did this deliberately." The driver finishes the turn and passes us on the way back toward the main drag. As he does so, he shakes his head and gives us an apologetic shrug. Coupled with a toothy grin, the driver's gesture reveals a touch of mainlander frustration but is tempered with a protective dose of islander fatalism. Coming through the sealed windows of our rental car, it spells out a succinct message: "Hey, it's Maui; what are you gonna do?" Stallman can take it no longer. "Don't you fucking smile!" he shouts, fogging up the glass as he does so. "It's your fucking fault. This all could have been so much easier if we had just done it my way." Stallman accents the words "my way" by gripping the steering wheel and pulling himself towards it twice. The image of Stallman's lurching frame is like that of a child throwing a temper tantrum in a car seat, an image further underlined by the tone of Stallman's voice. Halfway between anger and anguish, Stallman seems to be on the verge of tears. Fortunately, the tears do not arrive. Like a summer cloudburst, the tantrum ends almost as soon as it begins. After a few whiny gasps, Stallman shifts the car into reverse and begins executing his own U-turn. By the time we are back on the main drag, his face is as impassive as it was when we left the hotel 30 minutes earlier. It takes less than five minutes to reach the next cross-street. This one offers easy highway access, and within seconds, we are soon speeding off toward Pa'ia at a relaxing rate of speed. The sun that once loomed bright and yellow over Stallman's left shoulder is now burning a cool orange-red in our rearview mirror. It lends its color to the gauntlet wili wili trees flying past us on both sides of the highway. For the next 20 minutes, the only sound in our vehicle, aside from the ambient hum of the car's engine and tires, is the sound of a cello and a violin trio playing the mournful strains of an Appalachian folk tune. Endnote About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Sam Williams (2004). Free as in Freedom: Richard Stallman's Crusade for Free Software. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/cache/epub/5768/pg5768.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

A Brief Journey Through Hacker Hell

Un breve viaje por el infierno de los hackers

Un bref voyage à travers l'enfer des hackers

हैकर नर्क के माध्यम से एक संक्षिप्त यात्रा

ハッカー地獄の短い旅

Uma breve jornada pelo inferno dos hackers

Hành trình ngắn qua Hacker Hell

黑客地狱之旅

The Jungle: Chapter I 

The Human Side of Animals by Royal Dixon is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here: [LINK TO TABLE OF LINK] . Chapter II - The Human Side of Animals CHAPTER II. ANIMAL MUSICIANS "Nay, what is Nature's self, But an endless strife towards Music, euphony, rhyme?" —Watson. The great thinkers of the age believe that the world is one marvellous blending of innumerable and varied voices. This unison of sound forms the great music of the spheres, which the poets and philosophers have written so much about. Even from a purely scientific point of view, there is no denying that this music exists. Aviators tell us that when they listen from a distance to the myriads of noises and sounds that arise over a great city, these are all apparently lost in a modulated hum precisely like the vibrations of an immense tuning-fork, and appearing as but a single tone. Thus the immense noise going from our world is musically digested into one tone, and the aviator soaring above the earth hears only the one sound—the music of the spheres. The deep appreciation that animals have for music is becoming a generally known fact among those who have studied them closely. Every one must admit that there is much truth in the old saying that "music hath charms to soothe the savage breast." Music is composed of vibrations, which act with great power upon the nervous system of men and animals alike. Each is affected according to his particular physical and mental development. Professor Tarchanoff has made a careful study of the influence of music upon men and animals. He has demonstrated, by means of a machine which carefully registers the various activities of the hands and fingers, that when the hands are so tired and fatigued that they cannot make any marks except a straight line on the cylinder which registers the movements, music will so stimulate the nerves as to cause all fatigue to disappear. And as soon as the fingers again touch the cylinder, they begin to draw lines of various kinds and heights, thus proving that the music had rested the fingers and placed them under control. Various kinds of music were used: that of a melancholy nature had precisely the opposite effect to that of a lively, cheerful character; the nerves of the hands could either be contracted or expanded according to the nature of the music. Like all real scientists, Professor Tarchanoff does not claim to give any positive explanation of these facts. He believes, however, that the voluntary muscles act in the same relation to the music as the heart—that is, that cheerful, happy music affects the excito-motor nerves, sets up a vibration in those nerves which produces cheer and good feeling; while sad, morbid music plays along the depressant nerves and produces sadness and depression. In view of these facts, it is easy to see how animals, with their nervous temperaments and ready response to outside stimuli, are greatly influenced by various kinds of music. It is scientifically recognised that music tends to increase the elimination of carbonic acid and increases not only the consumption of oxygen, but even the activities of the skin. There is no doubt that good music at meal time aids the digestion. American Museum of Natural History, New York MONKEYS ARE THE MOST MUSICAL OF ALL ANIMALS. WHEN THEY CONGREGATE FOR "CONCERTS," AS SOME OF THE TRIBES DO, THE AIR IS FILLED WITH WEIRD STRAINS OF MONKEY-MUSIC. CATS, UNLIKE DOGS, ARE VERY FOND OF MUSIC. AND IT HAS BEEN PROVED THAT THEIR MUSIC-SENSE CAN BE DEVELOPED TO A REMARKABLE DEGREE. Cats have a species of unbeautiful music all their own, generally produced at late hours of the night on the house tops, garden walls, and in the alleys of our dwellings. Miss Cat's songs are far too chromatic to be appreciated by human ears; as a result her concertos and solos are rarely spoken of by human critics. However, Nature does sometimes produce a Tetrazzini, Alice Neilson, or Ca ruso, in the form of a cat, which really delights in harmonious combinations of sound. I know, for instance, of a cat called "Nordica" owned by Presson Miller, who apparently takes the greatest delight in hearing good vocal and instrumental music. Another well-educated musical cat belongs to a friend who plays a guitar. This cat delights in touching the strings with his dainty, soft paws, and springs with delight as the notes are produced. The Animal World speaks of five musical cats, which were carried to various parts of the world and exhibited as "bell-ringers," and their owner made a fortune out of their concerts. Five bells were suspended from a hoop, which hung above the stage, and to each bell was attached a small rope. At a given signal, each cat would seize a bell and give it a pull. This was done with such perfect time and spirit that one might well believe it was the work of human musicians and not of cats. Cows are responsive to certain kinds of music. A funeral march makes them sad, and ragtime so disturbs them that they give but little milk. The newspapers claim that Charles W. Ward, who owns a ranch near Eureka, California, says that the right kind of music will increase the production of milk, and that he uses a phonograph in the dairy barn. A friend, who has travelled much, tells the story of a musical cow. He, in company with two other friends, was coming up a river in a small boat singing. Just as they turned a bend, they saw a small brown cow, suckling her calf, along with several other cows in a nearby pasture. The cow seemed so fascinated with the music that she plunged into the water and waded up to her head trying to reach the boat. As they rowed along, she ran up and down the bank, cutting capers in a most astonishing manner and lowing and bellowing in testimony of her delight in the music. She would leap, skip, roll on the grass, paw up the earth, like an angry bull, and chase off like a playful kitten, always with a low plaintive bellow as a final farewell. These friends often rowed up the river just to see if the musical cow was there, and she always greeted them in the usual appreciative manner. Lions and tigers are proverbially fond of music. Professional trainers tell us that these animals, when tamed, will not do their stunts without the accompaniment of music. The story is told of a group of tigers which recently refused to perform, because the musicians, while the performance was going on, went on a strike. At once when the music ceased, the animals returned to their respective seats and no amount of encouragement would induce them to continue their performance. No amount of threats would induce them to work without music. The trainer dared not punish them too severely, yet he feared that if they were not forced to perform, they might continue to strike. But such was not the case, for on the morrow when the musicians returned they acted as never before. Sheep, both tame and wild, are exceedingly fond of music, and the shepherds of Scotland have used it with their sheep for ages. When the shepherd plays upon his flute or bagpipe, they gather around him and listen apparently with great satisfaction; when the music ceases, they wander out to feed, and in the evening he leads them home by the single strains of his flute. Circus horses are not only fond of music, but are partial to certain tunes, and demand that these be played while they are doing their turn. If for any reason the band changes the tune during a performance, they immediately refuse to go on with their stunts. The original fountain of all music was based on the various voices and sounds of animals—and each musical instrument was originally devised to imitate these sounds. For all instruments—the bass drum, flute, clarinet, trombone, trumpet, violin, and even pipe organ—an animal may be mentioned that owns the fundamental tones in its voice, and which man has imitated. Castanets, for example, were imitations of the rattlesnakes; the first musical instruments of any savage tribe of men are made so as to represent the voices of the chief animals of that particular locality. Every animal of the higher order, with the exception of a few mute dogs that belong to very hot or cold climates, is possessed of some sort of musical tone, expressive of pain or joy, and by means of which he can express certain emotions. Darwin claimed that the voice of the gibbon, while extremely loud, was very musical; and Waterhouse said that this musician sang the scale with considerable accuracy, at least sufficiently well for a trained violinist to accompany him. Often when dogs hear music they howl, or attempt to sing. Some show a decided preference for certain kinds of music, and actually try to imitate it. Gross tells of a friend of his who had a dog with which he often gave performances. The dog would accompany his master, when he sang in falsetto, with howls that were unmistakably attempts at singing, and which readily adapted themselves to the pitch of the tone. This was a musical accomplishment of which he was very proud. On a subject of which so little is known, there are, of course, diverse opinions. Scheitlin believed that music is actually disagreeable to a dog, but he says that it may be questioned whether or not the dog does not in some way accompany it. And Romanes, the great animal authority, thought the same thing. He had a terrier, which accompanied him when he sang, and actually succeeded in following the prolonged notes of the human voice with a certain approximation to unison. Dr. Higgins, a musician, claimed that his large mastiff could sing to the accompaniment of the organ. Alix gives such positive examples that they are really marvellous: "Pere Pardies cites the case of two dogs that had been taught to sing, one of them taking a part with his master. Pierquin de Gembloux also speaks of a poodle that could run the scale in tune and sing very agreeably a fine composition of Mozart's My Heart It Sings at Eve." All the scientists in Paris, according to the same authority, went to see the dog belonging to Dr. Bennati, and hear it sing the scale, which it could do perfectly. Monkeys and apes most nearly approximate human musicians. In central Africa these animal tribes have musical centres where they congregate regularly for "concerts." Prof. Richard S. Garner, the noted authority on apes and monkeys, be lieves that the time has already come for the establishment of a school for their education. He would have the courses beginning with a kindergarten and advancing through as many grades as the students required. Prof. Garner furthermore believes that we have little understanding of the gorilla, and points out that these animals have a very happy and harmonious home life, the father being highly domestic and delighting in the company of his wife and children. It is not uncommon to find five or six generations in a certain district of the jungle. Their near kin, the chimpanzees, are equally clannish, but more musical. They come down from the branches of the trees, seating themselves on the dry leaves and assembling like an orchestra. After all are ready, they begin beating the leaves with their hands, at first very slowly, like the quiet prelude to a symphony, and gradually increasing in tempo until the grand crescendo is reached. Then, as if by the direction of an invisible leader, the music suddenly ceases. To deny that this is to them a real concert would lead us into extreme absurdities. In this connection it is interesting to note that when a baby is expected in the village, all music ceases until after its birth, when they again resume their periodic musical festivals. Hensel verifies this observation, and tells us of having seen apes come from their shelter in the early morning and congregate for a musical concert. "They repair," he says, "to the shelter of some gigantic monarch of the forest whose limbs offer facilities for walking exercises. The head of the family appropriates one of these branches and advances along it seriously, with elevated tail, while the others group themselves about him. Soon he gives forth soft single notes, as the lion likes to do when he tests the capacity of his lungs. This sound, which seems to be made by drawing the breath in and out, becomes deeper and in more rapid succession as the excitement of the singer increases. At last, when the highest pitch is reached, the intervals cease and the sound becomes a continuous roar, and at this point all the others, male and female, join in, and for fully ten seconds at a time the awful chorus sounds through the quiet forest. At the close the leader begins again with the detached sounds." Perhaps the most remarkable evidence of animals showing a comprehensive intelligence of musical pitch is demonstrated by cavalry horses. That they thoroughly understand it is clearly demonstrated by the fact that they will obey the calls of the bugle for cavalry evolutions without a moment's hesitation and with no suggestion from outside sources. These bugle calls are produced by a combination of four notes, each of a different pitch, and it is rarer to find a horse making a mistake in the musical orders given than it is for their masters. Rats and mice have a decided liking for music, as is attested by the fact that they appear as uninvited guests and also come as near the performer as possible. Mice, one would believe, love church music, for they often build their nests in pipe organs, thus being able to rear their children in both a musical and religious atmosphere! There is more truth than imagination in the story of the Pied Piper of Hamelin, which illustrates how they respond to the simple charms of music. Even donkeys betray tendencies toward musical efforts, and seem to be aroused by music at least temporarily to a higher mental plane than Balaam was inclined to ascribe to his wise ass. Not all of them sing equally well, but in Arizona the donkey is known as the "desert canary." If you were to spend a few glorious days in the Hopi village of Araibi, you would hear through the still, silent night their long nasal bray or song, and you would be convinced that the term is quite appropriate. You may not exactly like the tune, but you will concede that they sing! Society is just awakening to the joy and the sig nificance of community art. This is everywhere indicated by the great growing group of people who come together for a common music, either as a chorus or an orchestra or both. But in this field man has not yet attained such unity of communal effort as have the frogs. In the great swamps of the world myriads of them gather from miles around, conscious of one purpose, and by a marvellous understanding and co-operation create for themselves a symphony with beauties and harmonies of its own, and such as to stand unrivalled in man's musical world. In the great chorus are voices from the lowest bass of the croaking bullfrog, squatting in the marshes, to the myriads of tiny green tree tenors, between which are millions of altos, contraltos, sopranos, coloraturas and other voices not yet in our musical vocabulary. These are accompanied by all the sounds of our orchestra and innumerable others of such delicate shades and gradations as to defy the ear of man. If we listen to one of these concerts, we will quickly recognise the tones of every familiar instrument, such as the drum, pipe, horn, trombone, oboe, piccolo, 'cello, and violin. The greatest of these musical festivals directly precedes the mating season, and is a dramatic instance of a manifestation of an inner rhythm which corresponds to an external periodicity. Among the oldest traditions of the Eastern world are those of snake-charming by means of music. I have long been interested in this strange phenomenon of Nature, and in company with a brilliant young violinist visited a zoological park recently, and after securing permission from the head keeper, entered the snake-house. The violinist began by playing a few most sympathetic chords, first delicate and soft, then sad, then gay, slow or tremulous. Near us, coiled in his immense cage, was a large cobra—the snake which all legend claims is most easily influenced by music. Almost immediately after the music began, the cobra raised himself in a listening attitude, steadily gazed at us as though he were viewing the future, spread his immense hood, and slowly began to shake his head from side to side, as if he were trying to keep time to the music. As soon as the music would change, his attitude changed accordingly. Only after the music had ceased did he resume his normal position. The Indians agree that under the influence of various musical instruments, especially bagpipes, snake-charmers are able to get the snakes to come out from their homes among the old rocks and walls, and when they appear they seem perfectly dazed so that they can be easily captured. It is not well to have any kind of musical instrument played, when in a forest at night where there are dangerous snakes, lest they come to hear it. Snake-hunters always carry with them some kind of musical instrument, depending upon the kind of snakes they wish to capture. It seems that all are not equally fascinated by it. I have experimented with little effect upon a large rattler; it may have been that he was deaf. But he gave little evidence of being interested. We need not feel humiliated, then, for our animal kinspeople with their primitive music: we were monkeys, and before them we were reptiles, birds, fishes, even worms. But that was ages ago, and we have grown up and become better musicians. Evolution has chosen us as its favourites and given us every advantage in the struggle up the ladder of life. Our musical rivals of yesterday are as chorus people compared to Metropolitan Opera stars, with us. On this earth we reign supreme, we have conquered the earth, air, and water, annihilating time and distance. What more is there for us to learn of Nature's secrets? Only an understanding of our lower brothers, the animals. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Dixon, Royal, 2006. The Human Side of Animals. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/19850/19850-h/19850-h.htm#Page_18 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.

The Human Side of Animals: Chapter 2 - Animal Musicians

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