Robust Features for Effective Speech and Music Discrimination

Robust Features for Effective Speech and Music Discrimination Zhong-hua Fu1, Jhing-Fa Wang2 School of Computer Science Northwestern Polytechnical University, Xi’an, China1 Department of Electrical Engineering National Cheng Kung University, Tainan, Taiwan1, 2 mailfzh@mail.ncku.tw1, wangjf@csie.ncku.edu.tw2 Abstract Speech and music discrimination is one of the most important issues for multimedia information retrieval and efficient coding. While many features have been proposed, seldom of which show robustness under noisy condition, especially in telecommunication applications. In this paper two novel features based on real cepstrum are presented to represent essential differences between music and speech: Average Pitch Density (APD), Relative Tonal Power Density (RTPD). Separate histograms are used to prove the robustness of the novel features. Results of discrimination experiments show that these features are more robust than the commonly used features. The evaluation database consists of a reference collection and a set of telephone speech and music recorded in real world. Keywords: Speech/Music Discrimination, Multimedia Information Retrieval, Real Cepstrum. 1. Introduction In applications of multimedia information retrieval and effective coding for telecommunication, audio stream always needs to be diarized or labeled as speech, music or noise or silence, so that different segments can be implemented in different ways. However, speech signals often consist of many kinds of noise, and the styles of music such as personalized ring-back tone may differ in thousands ways. Those make the discrimination problem more difficult. A variety of systems for audio segmentation or classification have been proposed in the past and many features such as Root Mean Square (RMS) [1], Zero Crossing Rate (ZCR) [1,4,5], low frequency modulation [2,4,5], entropy and dynamism features [2,3,6], Mel Frequency Cepstral coefficients (MFCCs) have been used. Some features need high quality audio signal or refined spectrum detail, and some cause long delay so as not fit for telecommunication applications. While the classification frameworks including nearest neighbor, neural network, Hidden Markov Model (HMM), Gaussian Mixture Modal (GMM) and Support Vector Machine (SVM) have been adopted as the back end, features are still the crucial factor to the final performance. As shown in the following part of this paper, the discrimination abilities of some common features are poor with noisy speech. The main reason may explain as that they do not represent the essential difference between speech and music. In this paper, two novel features, called as Average Pitch Density (APD) and Relative Tonal Power Density (RTPD) are proposed, which are based on real cepstrum analysis and show better robustness than the others. The evaluation database consists of two different data sets: one comes from Scheirer and Slaney [5], the other is collected from real telecommunication situation. The total lengths for music and speech are about 37 minutes and 28.7 minutes respectively. The rest of this paper is organized as follows: Section 2 introduces the novel features based on real cepstrum analysis. Section 3 describes the evaluation database and the comparative histograms of different features. The discrimination experiments and their results are given in section 4. Section 5 concludes this paper. 2. Features Based …

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