Trends in audio signal feature extraction methods

By Garima Sharma, K. Umapathy, S. Krishnan. 2020

433 citations recorded by the source corpus.

Abstract Audio signal processing algorithms generally involves analysis of signal, extracting its properties, predicting its behaviour, recognizing if any pattern is present in the signal, and how a particular signal is correlated to another similar signals. Audio signal includes music, speech and environmental sounds. Over the last few decades, audio signal processing has grown significantly in terms of signal analysis and classification. And it has been proven that solutions of many existing issues can be solved by integrating the modern machine learning (ML) algorithms with the audio signal processing techniques. The performance of any ML algorithm depends on the features on which the training and testing is done. Hence feature extraction is one of the most vital part of a machine learning process. The aim of this study is to summarize the literature of the audio signal processing specially focusing on the feature extraction techniques. In this survey the temporal domain, frequency domain, cepstral domain, wavelet domain and time-frequency domain features are discussed in detail.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/7c90871865ba83dca2fe4e7e305b8af79651838a