Audio signals are often stored or transmitted in a compressed representation, which can pose a problem if there is a requirement to perform signal processing; it is likely it will be necessary to convert the signal back to a time domain representation, process, and then re-transform. This is timeconsuming and computationally intensive; it is potentially more efficient to apply signal processing while the signal remains in the transform domain. We have implemented a scheme whereby linear processing of the traditional type often instinctively understood by those working in the audio field may be applied to signals stored in a wavelet domain representation. Results are presented which demonstrate that the method produces the same output – to within the limits of machine precision – as timedomain processing, for less computational effort than would be required for the full explicit process through the time domain and back again. The potential benefits for linear effects processing (for example, EQ and sample-level delays and echoes) and also for non-linear processing such as dynamics processing, will be introduced and discussed.

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Common problems with current methods of musical note onset detection are detection of fast passages of musical audio, detection of all onsets within a passage with a strong dynamic range and detection of onsets of varying types, such as multi-instrumental music. We present a method that uses a subband decomposition approach to onset detection. An energy-based detector is used on the upper subbands to detect strong transient events. This yields precision in the time resolution of the onsets, but does not detect softer or weaker onsets. A frequency based distance measure is formulated for use with the lower subbands, improving detection accuracy of softer onsets. We also present a method for improving the detection function, by using a smoothed difference metric. Finally, we show that the detection threshold may be set automatically from analysis of the statistics of the detection function, with results comparable in most places to manual setting of thresholds.

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This paper presents a piano transcription system that transforms audio into MIDI format. Human knowledge and psychoacoustic models are implemented in a blackboard architecture, which allows the adding of knowledge with a top-down approach. The analysis is adapted to the information acquired. This technique is referred to as a prediction-driven approach, and it attempts to simulate the adaptation and prediction process taking place in human auditory perception. In this paper we describe the implementation of Polyphonic Note Recognition using a Fuzzy Inference System (FIS) as part of the Knowledge sources in a Blackboard system. The performance of the transcription system shows how polyphonic music transcription is still an unsolved problem, with a success of 45% according to the Dixon formula. However if we consider only the transcribed notes the success increases to 74%. Moreover, the results obtained in the paper presented in [1], show how the transcription can be used with success in a retrieval system, encouraging the authors to develop this technique for more accurate transcription results.

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