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book/_toc.yml

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- file: conclusions/applications
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- file: conclusions/concluding_remarks
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- part: Appendix
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- part: Resources
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chapters:
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- file: zzz_refs
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- file: appendix/resources
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- file: appendix/cite_this
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- file: appendix/acknowledgements
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- file: appendix/authors
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- file: resources/reading
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- file: resources/cite_this
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- file: resources/acknowledgements
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- file: resources/authors

book/resources/acknowledgements.md

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Acknowledgements
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================
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## Open Source Contributors
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This tutorial would not exist if not for the many people who have graciously
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contributed their time and expertise to the open source projects that we
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showcase. We want use this page to acknowledge the great work that they have
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done.
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### nussl
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- Ethan Manilow
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- Prem Seetharaman
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- Fatemeh Pishdadian
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- Nathan Shelly
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- Andreas Bugler
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- Bryan Pardo
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- Corey Grief
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- Daniel Felix Kim
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- Ben Kalish
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### Scaper
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- Justin Salamon
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- Prem Seetharaman
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- Duncan MacConnell
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- Mark Cartwright
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- Peter Li
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- Juan Pablo Bello
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### Other Projects
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Additionally, we want to thank the maintainers and contributors of these amazing
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open source projects:
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- librosa
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- matplotlib
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- numpy
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- scipy
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- scikit-learn
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- pytorch
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- pytorch-ignite
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- soundfile
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- mir_eval
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- open_unmix
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- asteroid
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- demucs
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- gif
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- gradio
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- pyloudnorm
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- Spleeter
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- Wave-U-Net
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## Images
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The authors have used the following images from the [Noun Project](https://thenounproject.com/):
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- "Mixing Board" by Nick Dominguez
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- "Overdrive Pedal" by Joel Wisneski
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- "Guitar Amplifier" by Premyslav
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- "Microphone" by Olena Panasovska
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- "Singer" by Gan Khoon Lay
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- "Bass Guitar" by Marvdrock
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- "Drum" by Hopkins
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- "Microphone" by IconMark
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- "piano keyboard" by b farias
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## Special Thanks
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The authors want to acknowledge the many members of the community who graciously
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let us use parts of their work in this book. Their names are placed throughout
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the text.
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We also want to thank these people, without whom we would not be able to write
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this book:
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- Bryan Pardo
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- Jonathan Le Roux
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- Gordon Wichern
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book/resources/authors.md

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About the Authors
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=================
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**Ethan Manilow** is a PhD candidate in Computer Science at Northwestern
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University under advisor Prof. Bryan Pardo. His research lies in the
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intersection of signal processing and machine learning, with a focus on source
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separation, automatic music transcription, and open source datasets and
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applications. Previously he was an intern at Mitsubishi Electric Research Labs
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(MERL) and at Google Magenta. He is one of the lead developers of nussl, an
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open source audio separation library.
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[website.](https://ethman.github.io/) [twitter.](https://twitter.com/ethanmanilow)
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**Prem Seetharaman** is a research scientist at Descript in San Francisco.
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Previously, he was a teaching fellow at Northwestern University, where he
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received his PhD in 2019 advised by Bryan Pardo. The objective of his research
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is to create machines that can understand the auditory world. He works in
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computer audition, machine learning, and human computer interaction. He is one
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of the lead developers of nussl, an open source audio separation library, and
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Scaper, a library for soundscape generation & augmentation.
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[website.](https://pseeth.github.io/) [twitter.](https://twitter.com/pseetharaman)
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**Justin Salamon** is a research scientist and member of the Audio Research
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Group at Adobe Research in San Francisco. Previously he was a senior research
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scientist at the Music and Audio Research Laboratory and Center for Urban
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Science and Progress of New York University. His research focuses on the
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application of machine learning and signal processing to audio & video, with
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applications in machine listening, representation learning & self-supervision,
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music information retrieval, bioacoustics, environmental sound analysis and
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open-source software & data. He is the lead developer of Scaper, a library for
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soundscape generation & augmentation.
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[website.](https://www.justinsalamon.com/) [twitter.](https://twitter.com/justin_salamon)

book/resources/glossary.md

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Glossary
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========
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<!-- We have to alphabetize this ourselves.. :-/ -->
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```{glossary}
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Beamforming
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A related, but separate area of research that tries to estimate isolate sources
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using multiple recordings of an auditory scene from different angles and locations.
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DFT
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"Discrete Fourier Transform"
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Downsample
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Resample an audio signal such that the sample rate is lower than it originally
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was.
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Mono
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An audio signal is _monophonic_, or _mono_, if it only has one channel of audio data.
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Monophonic
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An audio signal is _monophonic_, or _mono_, if it only has one channel of audio data.
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Sample
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A single measurement from a continuous-time signal that becomes a
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discrete-time signal.
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Sample Rate
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The number of digital samples that occur per second.
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Short-time Fourier Transform (STFT)
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A time-frequency representation that has phase and magnitude.
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Stereo
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An audio signal is _stereophonic_, or _stereo_, if it has exactly two channels of audio data.
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Stereophonic
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An audio signal is _stereophonic_, or _stereo_, if it has exactly two channels of audio data.
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STFT
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"Short-time Fourier Transform"
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TF
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"Time-Frequency"
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TF bin
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A single value in a Time-Frequency matrix.
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TF Representation
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A general term for any 2D representation of audio that represents time along
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one axis and frequency (or pitch) along the other. This might be an STFT, a
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spectrogram, CQT, LogMel-Spectrogram, etc.
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Upsample
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Resample an audio signal such that the sample rate is higher than it originally
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was.
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Waveform
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A
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```

book/resources/reading.md

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Additional Reading
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==================
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## Source Separation
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Additional resources for learning more about source separation:
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- [SigSep Website](https://sigsep.github.io/)
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- [DCASE Task 4 - Environmental Sound Separation](http://dcase.community/challenge2020/task-sound-event-detection-and-separation-in-domestic-environments-results)
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- [Emmanuel Vincent's website](https://members.loria.fr/EVincent/keynotes-tutorials-and-press/)
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## Learning about Music Information Retrieval (MIR)
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Other resources useful for further exploration of MIR topics:
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- [Music Information Retrieval](https://musicinformationretrieval.com/) website
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- [Prof. Bryan Pardo's class on Machine Perception of Music](https://interactiveaudiolab.github.io/teaching/eecs352.html)
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- [Prof. Alexander Lerch's Audio Content Analysis](https://www.audiocontentanalysis.org/) website
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- [Coursera Course on Audio Signal Procesing for Musical Applications](https://www.coursera.org/learn/audio-signal-processing)
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- [Prof. Julius O. Smith III's Online DSP Textbooks](https://ccrma.stanford.edu/~jos/)
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- [Fabian-Robert Stöter's Awesome Python Scientific Audio Github list](https://github.com/faroit/awesome-python-scientific-audio)

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