Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

TLDR

Audio signals are nonstationary, making time‑ or frequency‑only analyses inadequate, so joint time‑frequency approaches are preferred for tasks such as compression, content‑based retrieval, classification, and protection of digital audio. This paper aims to present a comprehensive set of time‑frequency methods that address these audio processing tasks. The authors demonstrate the benefits of time‑frequency techniques through a psychoacoustically informed coding scheme, music and environmental sound classification, audio fingerprinting, and watermarking.

Abstract

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.