Master thesis. Report and code available in the Github repository.

Presentation at EuroPython2019. Video recording, notes

Report and lecture at NMBU Data Science.

Report | Slides

With example code in Python

• Loading Youtube audio data with youtube-dl and librosa
• Extracting fixed-size analysis windows from audio
• Classifying an audio clip of many analysis windows using Keras Timedistributed and GlobalAveragePooling
• Classifying an audio clip by voting over analysis windows. Mean/majority voting.
• Annotating/labeling audio data using Audacity
• Preprocessing audio into mel-spectrograms
• Multi-core preprocessing of audio files using joblib
• Compute MFCC or mel-spectrogram from existing STFT spectrograms
• Converting mel-spectrograms into PNG images

Rough notes on various topics.

• Applications. Practical applications of Machine Hearing
• Tasks. Established problem formulations
• Features. Feature representations
• Preprocessing. Preprocessing techniques
• DCASE2018. Notes from DCASE2018 challenge and conference
• Commercial solutions. Companies and products in Machine Hearing
• Compressive Sensing.

Useful resources to learn more.

• Computational Analysis of Sound Scenes and Events. Tuomas Virtanen, Mark D. Plumbley, Dan Ellis. 2018.
• Human and Machine Hearing - Extracting Meaning from Sound. Richard F. Lyon. 2017, revised 2018.
• An Introduction to Audio Content Analysis - Applications in Signal Processing and Music Informatics. Alexander Lerch. 2012. Companion website: https://www.audiocontentanalysis.org/
• Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing). Francesco Camastra, 3 sections. From Perception to Computation, Machine Learning, Applications.

• CSC 83060: Speech and Audio Understanding. http://mr-pc.org/t/csc83060/ Brooklyn College (CUNY).

Feature extraction

• librosa. The go-to Python module.
• essentia. C++ library, with Python bindings. Lots of Music Analysis extractors. Used by FreeSound and Acousticbrainz.
• kapre. On-demand GPU computation of melspectrograms, for Keras
• torchaudio.

Data Augmentation

• muda: Python library for augmenting annotated audio data

• Audio Classification. http://www.cs.tut.fi/~sgn24006/PDF/L04-audio-classification.pdf Covers low-level features, MFCC. Classification by distance metrics. GMM. HMM.
• Speech Signal Analysis, Lecture 2. January 2017, Hiroshi Shimodaira and Steve Renals. ! great diagrams of audio discretization, mel filters, wide versus narrow-band spectrograms.

• Kaggle Whale detection
• Kaggle FreeSound tagging 2018
• Kaggle FreeSound
• DCASE2014
• DCASE2018
• DCASE2019

• Audio cats and dogs (Kaggle)
• Heartbeat anomalies (Kaggle).
• Respiratory sounds (Kaggle)

• https://mircommunity.slack.com/ - Music Information Retrieval

• Awesome Deep Learning Music
• Fast.ai forums: Deep Learning with Audio. Large lists of resources, both in first post and "popular links". Feb 2019, 315 replies over 4 months.