Panos Kudumakis

Classification of audio signals using statistical features on time and wavelet transform domains

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving an overall classification accuracy of 91.67%, using either the minimum distance classifier or the least squares minimum distance classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7.

On the performance of wavelets for low bit rate coding of audio signals

The performance of some different wavelet families, including for comparison a well known family of QMFs, is investigated for low bit rate coding of audio signals. For the assessment of the coding gain of these wavelets, both octave and uniform subband coding schemes have been evaluated, using both constant and dynamic bit allocation, with and without entropy noiseless Huffman coding. The influence of complexity of these wavelets, in terms of number of filter coefficients, against the quality of the decompressed audio signals in terms of segmental-SNR (dB), is presented, at different bit rates. In addition, this evaluation suggests that perceptually transparent quality of monophonic signals can be achieved at 24 kbits/sec (Fs=8 kHz, 3 bits/sample) for speech applications and at 64 kbits/sec (Fs=48 kHz, 1.33 bits/sample) for music related applications, as in digital audio transmission and storage.

Wavelet packet based scalable audio coding

Scalability, a well known concept in video coding, has only recently been introduced to audio coding. In this paper, a novel approach to a two-stage wavelet packet based scalable audio coding system is presented. Two different structures have been designed and implemented, one in the time-domain, and its dual in the wavelet-domain; these are compared with an MPEG based scalable codec. Results at different bit-rates are shown, while trade-offs and limitations together with future developments for further reduced bit-rates are discussed.

On the compression obtainable with four-tap wavelets

The performance obtainable with four-tap wavelet filters for low bit rate audio coding is presented. For the investigation and comparison of the performance of these wavelet filters, a codec model has been designed and implemented based on wavelet packet algorithm and the model of auditory perception.

Usage of short wavelets for scaleable audio coding

In this paper, an in depth investigation and comparison of the performance obtainable with short wavelet filters for low bit rate perceptual audio coding is presented. This a priori knowledge of the short wavelet filters performance evaluation open new horizons in their usage, especially, when combined with the Moving Pictures Expert Group (MPEG-4) requirements for segmental signal to noise ratio scalable audio coding.

High quality audio coding for mobile multimedia communications

Publisher Summary This chapter highlights the growing importance of research into audio to the fast-moving field of mobile multimedia. The main body of this chapter presents results from two projects that examine high-quality, low bit-rate coding of audio, both music and speech. One approach uses wavelets, which offer advantages over polyphase filter banks and discrete cosine transform (DCT) in MPEG music coding at low bit rates. The other approach uses the long established linear predictive coding (LPC) technique, but in a modified guise, with orders significantly greater than 10. It also uses least mean squares techniques to fit lines in time-frequency space to the line spectral pair (LSP) representation of LPC. The first section offers an introduction to audio coding and processing in the broadest sense as is applicable to mobile multimedia. This chapter emphasizes that in a complete mobile multimedia (MMM) system, the designer needs to take account of more than just the most effective coding technique. Following this, a new approach to speech coding is described and some experimental results presented. Some attention is paid to the means by which high-quality, high-order LPC speech may be resynthesized. Finally, the principles of the use of wavelets in audio coding are covered and these too are supported by results.