pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

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 retri...

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Bibliographic Details
Published in:PloS one Vol. 10; no. 12; p. e0144610
Main Author: Giannakopoulos, Theodoros
Format: Journal Article
Language:English
Published: United States Public Library of Science 11.12.2015
Public Library of Science (PLoS)
Subjects:
Acoustic Stimulation
Analysis
Animals
Audio data
Audio signals
Classification
Data processing
Discriminant analysis
Eating behavior
Emotion recognition
Feature extraction
Fourier transforms
Habits
Humans
Information processing
Information retrieval
Information Storage and Retrieval
International conferences
Language
Libraries
Libraries, Digital
Medical screening
Mental depression
Multimedia
Music
Pattern Recognition, Automated
Python
Recommender systems
Segmentation
Short term
Signal analysis
Software
Sound
Sound analyzers
Speech
Speech - physiology
Speech recognition
Surveillance equipment
Surveillance systems
Greece
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Description
Summary: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.
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Competing Interests: The author has declared that no competing interests exist.
Conceived and designed the experiments: TG. Performed the experiments: TG. Analyzed the data: TG. Contributed reagents/materials/analysis tools: TG. Wrote the paper: TG.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0144610