pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis
By Theodoros Giannakopoulos. PLoS ONE, 2015
464 citations recorded by the source corpus.
Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/7c9856d190cf1fd2e8a54091f5bb4ac89db6202a
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In Artificial Intelligence
- Tuomas Virtanen
- Annamaria Mesaros
- Toni Heittola
- Benjamin Elizalde
- Bhiksha Raj
- Emmanuel Vincent
- Dinesh Manocha
- Hervé Bredin
In Signal Processing
- Tuomas Virtanen
- Bhiksha Raj
- Toni Heittola
- Ankit Shah
- Annamaria Mesaros
- Benjamin Elizalde
- Emmanuel Vincent
- Aleksandr Diment
Citations in the corpus
- DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System (2017, methodology): “First, a semi-supervised segmentation [28] was performed using an SVM trained to classify high-energy and low-energy frames.”
- Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues (2020, methodology): “To extract speech features, we use pyAudioAnalysis [25] to extract 13 Mel Frequency Cepstral Coefficients (MFCC) speech features.”
- Pyannote.Audio: Neural Building Blocks for Speaker Diarization (2019, background): “…ALIZ´E and its LIA SpkSeg extension for speaker diarization are written in C++ and do not provide recent deep learning approaches for speaker diarization [7]; pyAudioAnalysis is written in Python and addresses more general audio signal analysis, though it can be used for speaker diarization [8].”