Tempo, micro time and time signature are basic rhythmic features for a wide range of music. Their automated extraction from music is needed for a number of applications, e.g. automatic transcription and music information retrieval systems, authoring tools, entertainment applications, musicological analysis or educational purposes. The presented analysis procedure estimates tempo, micro time and time signature from percussive music. From 117 excerpts of eight seconds length, 84.6% of the tempo values, 83.8% of the micro time and 73.5% of the time signatures were estimated correctly.

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A supervised learning approach to ambience extraction from onechannel audio signals is presented. The extracted ambient signals are applied for the blind upmixing of musical audio recordings to surround sound formats. The input signal is processed by means of short-term spectral attenuation. The spectral weights are computed using a low-level feature extraction process and a neural network regression method. The multi-channel audio signal is generated by feeding the computed ambient signal into the rear channels of a surround sound system.

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A novel method for scaling the level of the virtual center in audio signals is proposed. The input signals are processed in the time-frequency domain such that direct sound components having approximately equal energy in all channels are amplified or attenuated. The real-valued spectral weights are obtained from the ratio of the sum of the power spectral densities of all input channel signals and the power spectral density of the sum signal. Applications of the presented method are upmixing two-channel stereophonic recordings for its reproduction using surround sound set-ups, stereophonic enhancement, dialogue enhancement, and as preprocessing for semantic audio analysis.

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