Content-based audio classification and retrieval using a fuzzy logic system: towards multimedia search engines

Abstract

In recent years, available audio corpora are rapidly increasing from fast growing Internet and digital libraries. How to classify and retrieve sound files relevant to the user's interest from large databases is crucial for building multimedia web search engines. In this paper, content-based technology has been applied to classify and retrieve audio clips using a fuzzy logic system, which is intuitive due to the fuzzy nature of human perception of audio, especially audio clips with mixed types. Two features selected from various extracted features are used as input to a constructed fuzzy inference system (FIS). The outputs of the FIS are two types of hierarchical audio classes. The membership functions and rules are derived from the distributions of extracted audio features. Speech and music can thus be discriminated by the FIS. Furthermore, female and male speech can be separated by another FIS, whereas percussion can be distinguished from other music instruments. In addition, we can use multiple FISs to form a ''fuzzy tree'' for retrieval of more types of audio clips. With this approach, we can classify and retrieve generic audios more accurately, using fewer features and less computation time, compared to other existing approaches.

Content-Based Multimedia Retrieval

“…The work of Liu [56], for example, represents a first attempt at building a multimedia search engine for the Internet: a fuzzy inference system for audio classification and retrieval. Support vector machines (SVMs) have also been employed with a multiclassification strategy [40] to address this problem.…”

Direct access in content-based Audio Information Retrieval: A state of the art and challenges

“…Table III lists the recent audio classification researches in this decade. In 2002, Liu proposed the fuzzy inference system [23] for audio classification and retrieval, as a first step towards a multimedia search engine for the Internet. Liu focused on classification and retrieval of two major audios, speech and sounds of music instruments.…”

Tree-based versus distance-based key recognition in musical audio

“…Both [3] and [7] discuss the use of binary classification trees for the classification of musical instruments. Classification tree techniques have also been applied for musical genre classification [26] or content-based audio classification [20,16].…”

“…Taking into account the binary structure of the classification tree, it is clear that a reduction is possible from 24 classes into 16,8,4 or 2 clusters of classes.…”