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Time-frequency visual representation and texture features for audio applications: a comprehensive review, recent trends, and challenges

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Abstract

The conventional audio feature extraction methods employed in the audio analysis are categorized into time-domain and frequency-domain. Recently, a new audio feature extraction approach using time-frequency texture image is developed and utilized for different applications. In this approach, the input audio signal is first converted into a time-frequency image, and then textural features are extracted from the visual representation. The distinctive two-dimensional time-frequency visualization textural descriptors can produce better features for improved audio detection and classification. In this article, a comprehensive review of state-of-the-art techniques used for audio detection and classification is presented. The generalized architecture of time-frequency texture feature extraction approaches in audio classification algorithms is presented first. Based on a review of over 70 papers, the key contributions in the area of time-frequency representations of various researchers are highlighted in addition to the textural features. This survey also compares and analyzes the existing experimental algorithms proposed for various audio classification tasks. Finally, the critical challenges and limitations with different visual representations are highlighted, along with potential future research directions.

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  1. Department of Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil deemed to be University, Nerul, 400706, Navi Mumbai, Maharashtra, India

    Yogita D. Mistry, Gajanan K. Birajdar & Archana M. Khodke

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Mistry, Y.D., Birajdar, G.K. & Khodke, A.M. Time-frequency visual representation and texture features for audio applications: a comprehensive review, recent trends, and challenges. Multimed Tools Appl 82, 36143–36177 (2023). https://doi.org/10.1007/s11042-023-14734-1

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