Spectrogram features for audio and speech analysis

2026-03-16

AI Summary

This study investigates how to select spectrogram representations that align with the architecture of downstream classifiers to enhance performance in audio and speech analysis tasks. By systematically exploring the design space of spectrograms—encompassing time–frequency resolution, temporal span, and element-wise scaling—and integrating convolutional neural networks with time–frequency analysis techniques, the work comprehensively evaluates diverse spectrogram configurations across multiple tasks. The findings reveal key principles for the co-optimization of front-end features and back-end models, delineate the suitability of various spectrogram representations for specific scenarios, and provide both theoretical grounding and practical guidance for task-oriented, efficient feature engineering and model design.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the primary motivator for spectrogram-based representations was their ability to present sound as a two dimensional signal in the time-frequency plane, which not only provides an interpretable physical basis for analysing sound, but also unlocks the use of a wide range of machine learning techniques such as convolutional neural networks, that had been developed for image processing. A spectrogram is a matrix characterised by the resolution and span of its two dimensions, as well as by the representation and scaling of each element. Many possibilities for these three characteristics have been explored by researchers across numerous application areas, with different settings showing affinity for various tasks. This paper reviews the use of spectrogram-based representations and surveys the state-of-the-art to question how front-end feature representation choice allies with back-end classifier architecture for different tasks.

Problem

• spectrogram
• audio analysis
• speech analysis
• feature representation
• classifier architecture

Innovation

• spectrogram
• feature representation
• deep learning
• audio analysis
• speech analysis