The task of separating a mix of several inter-weaving melodies from a mono recording into multiple tracks is attempted by filtering in the spectral domain. The transcribed score is provided in MIDI format a priori. In each time frame a filter is constructed for each instrument in the mix, whose effect is to filter out all harmonics of that instrument from the DFT spectrum. The complication of overlapping harmonics arising from separate notes is discussed and two filter shapes that were found to be fairly successful at separating overlapping harmonics are presented. In comparing the separated audio tracks to the original instrumental parts, signalto-residual ratios (SRR’s) in excess of 20 dB have been achieved. Audio demonstrations are on the internet [1].

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The task of extracting harmonic content of multiple pitched sources from a mono audio mix has been investigated on several occasions [1, 2, 3, 4]. However, most pitched notes contain an inharmonic component, which is an important perceptual attribute of the sound. This content is usually not dealt with during separation. It would also be interesting in its own right to develop separation techniques for extracting percussive sounds for polyphonic mixes. This paper describes an attempt at separating overlapping impulsive content of multiple sources from a mono mix. The method uses an interpolation within individual frequency bands of the decaying noise envelope of each source across overlapping sections with other sources. Three analysis methods determining the distribution of these bands were tested: the DFT followed by processing in Bark bands, the discrete wavelet transform (DWT), and the dyadic wavelet packet transform (DWPT).

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Acoustic feedback is a recurrent problem in live sound reinforcement scenarios. Many attempts have been made to produce an automated feedback cancellation system, but none have seen widespread use due to concerns over the accuracy and transparency of feedback howl cancellation. This paper investigates the use of the Magnitude Slope Deviation (MSD) algorithm to intelligently identify feedback howl in live sound scenarios. A new variation on this algorithm is developed, tested, and shown to be much more computationally efficient without compromising detection accuracy. The effect of varying the length of the frequency spectrum history buffer available for analysis is evaluated across various live sound scenarios. The MSD algorithm is shown to be very accurate in detecting howl frequencies amongst the speech and classical music stimuli tested here, but inaccurate in the rock music scenario even when a long history buffer is used. Finally, a new algorithm for setting the depth of howl-cancelling notch filters is proposed and investigated. The algorithm shows promise in keeping frequency attenuation to a minimum required level, but the approach has some problems in terms of time taken to cancel howl.

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