<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Sound is the result of mechanical vibrations that set air molecules in motion, causing variations in air pressure that propagate as pressure waves. Represented as waveforms, these visual snapshots of sound reveal some of its characteristics. While waveform analysis offers limited insights, audio features provide a quantitative and structured way to describe sound, enabling data-driven analysis and interpretation. Different audio features capture various aspects of sound, facilitating a comprehensive understanding of the audio data. By leveraging audio features, machine learning models can be trained to recognize patterns, classify sounds, or make predictions, enabling the development of intelligent audio systems. Time-domain features, e.g., amplitude envelope, capture events from raw audio waveforms. Frequency domain features, like band energy ratio and spectral centroid, focus on frequency components, providing distinct information. In this paper, we will describe three time-domain and three frequency-domain features that we consider crucial and widely used. We will illustrate the suitability of each feature for specific tasks and draw general conclusions regarding the significance of sound features in the context of machine learning.</p>
</abstract>

Sound is the result of mechanical vibrations that set air molecules in motion, causing variations in air pressure that propagate as pressure waves. Represented as waveforms, these visual snapshots of sound reveal some of its characteristics. While waveform analysis offers limited insights, audio features provide a quantitative and structured way to describe sound, enabling data-driven analysis and interpretation. Different audio features capture various aspects of sound, facilitating a comprehensive understanding of the audio data. By leveraging audio features, machine learning models can be trained to recognize patterns, classify sounds, or make predictions, enabling the development of intelligent audio systems. Time-domain features, e.g., amplitude envelope, capture events from raw audio waveforms. Frequency domain features, like band energy ratio and spectral centroid, focus on frequency components, providing distinct information. In this paper, we will describe three time-domain and three frequency-domain features that we consider crucial and widely used. We will illustrate the suitability of each feature for specific tasks and draw general conclusions regarding the significance of sound features in the context of machine learning.

An Overview on Sound Features in Time and Frequency Domain

Computer Science and Electrical and Electronics Engineering Department, Faculty of Engineering

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"An Overview on Sound Features in Time and Frequency Domain"}

Sound is the result of mechanical vibrations that set air molecules in motion, causing variations in air pressure that propagate as pressure waves. Represented...

An Overview on Sound Features in Time and Frequency Domain

Online veröffentlicht: 19. Dez. 2023

Seitenbereich: 45 - 58

DOI: https://doi.org/10.2478/ijasitels-2023-0006

Schlüsselwörter
sound, feature extraction, machine learning

© 2023 Constantin Constantinescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Overview on Sound Features in Time and Frequency Domain

Online veröffentlicht: 19. Dez. 2023

Seitenbereich: 45 - 58

DOI: https://doi.org/10.2478/ijasitels-2023-0006

Schlüsselwörtersound, feature extraction, machine learning

© 2023 Constantin Constantinescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
sound, feature extraction, machine learning