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This paper describes the low-complexity 14 kHz audio coding algorithm which has been recently standardized by ITU-T as Recommendation G.722.1 Annex C ("G.722.1C"). The algorithm is an extension to ITU-T Recommendation G.722.1 and doubles the G.722.1 algorithm to permit 14 kHz audio bandwidth using a 32 kHz audio sample rate, at 24, 32, and 48 kbit/s. The G.722.1C codec features very high audio quality and extremely low computational complexity compared to other state-of-the-art audio coding algorithms. This codec is suitable for use in video conferencing and teleconferencing, and Internet streaming applications. Subjective test results from the characterization phase of G.722.1 C are also presented in the paper

An audio enhancement system is presented to improve the sound quality, especially the bass reproduction in the automobile cabin. High quality audio reproduction in cabin can be difficult due to a number of factors, including the noise in the cabin, performance, size of the loudspeakeers in the car. The proposed system uses digital singal processing techniques with the built-in car audio system to tune and make use of the engine noise. The main problems are the tracking of the engine noise and tune the noise to match the audio signal. In this paper, we propose a solution based on frequency sampling filters to track and extract the audio signal, and a multi-frequency active noise equalizer to tune the engine noise. The results showed that the proposed system could be a promising approach for solving the bass audio reproduction, together with the noise control problems in automobile.

This paper demonstrates the importance of temporal sequences for passage-level music information retrieval. A number of audio analysis problems are solved successfully by using models that throw away the temporal sequence data. This paper suggests that we do not have this luxury when we consider a more difficult problem: that is finding musically similar passages within a narrow range of musical styles or within a single musical piece. Our results demonstrate a significant improvement in performance for audio similarity measures using temporal sequences of features, and we show that quantizing the features to string-based representations also performs well, thus admitting efficient implementations based on string matching

The problem of generative process tracking involves detecting and adapting to changes in the underlying generative process that creates a time series of observations. It has been widely used for visual background modelling to adaptively track the generative process that generates the pixel intensities. In this paper, we extend this idea to audio background modelling and show its applications in surveillance domain. We adaptively learn the parameters of the generative audio background process and detect foreground events. We have tested the effectiveness of the proposed algorithms using synthetic time series data and show its performance on elevator audio surveillance