microsoft audio processing music classification

Chilton, 'Audio classification using acoustic images for retrieval from multimedia databases,' in Proceedings of the 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications, vol. Loughlin, 'Time-frequency-based classification,' in Advanced Signal Processing Algorithms, Architectures, and Implementations VI, vol. Nam, 'Classification of music signals in the visual domain,' in Proceedings of the COSTG6 Conference on Digital Audio Effects, 2001.

Posch, 'Positive time-frequency distribution functions,' IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. Chu, 'An improved Hilbert-Huang transform and its application in vibration signal analysis,' Journal of Sound and Vibration, vol. Daubechies, 'Wavelet transform, time-frequency localization and signal analysis,' IEEE Transactions on Information Theory, vol. Williams, 'Improved time-frequency representation of multicomponent signals using exponential kernels,' IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.

Gribonval and Rennes IRISA-INRIA, 'Fast matching pursuit with a multiscale dictionary of Gaussian chirps,' IEEE Transactions on Signal Processing, vol. Cohen, 'Time-frequency distributions-a review,' Proceedings of the IEEE, vol. Zhang, 'Matching pursuits with time-frequency dictionaries,' IEEE Transactions on Signal Processing, vol. Mallat, A Wavelet Tour of Signal Processing, Academic Press, New York, NY, USA, 1998. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas.

In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals.

Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment.

Microsoft audio processing music classification