Download Real-Time Separation Of Transient Information In Musical Audio Using Multiresolution Analysis Techniques
Whilst musical transients are generally acknowledged as holding much of the perceptual information within musical tones, most research in sound analysis and synthesis tends to focus on the steady state components of signals. A method is presented which separates the noisy transient information from the slowly time varying steady state components of musical audio. Improvements of using adaptive thresholding, and multiresolution analysis methods are then illustrated. It is shown that by analyzing the resulting transient information only, current onset detection algorithms can be improved considerably, especially for those instruments with noisy attack information, such as plucked or struck strings. The idea is then applied to audio processing techniques to enhance or decrease the strength of note attack information. Finally, the transient extraction algorithm (TSS) is applied to time-scaling implementation, where the transient and noise information is analyzed so that only steady state regions are stretched, yielding considerably improved results.
Download A Hybrid Approach to Musical Note Onset Detection
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.
Download Complex domain onset detection for musical signals
We present a novel method for onset detection in musical signals. It improves over previous energy-based and phase-based approaches by combining both types of information in the complex domain. It generates a detection function that is sharp at the position of onsets and smooth everywhere else. Results on a handlabelled data-set show that high detection rates can be achieved at very low error rates. The approach is more robust than its predecessors both theoretically and practically.
Download A comparison Between Fixed and Multiresolution Analysis for Onset Detection in Musical Signals
A study is presented for the use of multiresolution analysis-based onset detection in the complex domain. It shows that using variable time-resolution across frequency bands generates sharper detection functions for higher bands and more accurate detection functions for lower bands. The resulting method improves the localisation of onsets on fixed-resolution schemes, by favouring the increased time precision of higher subbands during the combination of results.
Download A Maximum Likelihood Approach to Blind Audio De-Reverberation
Blind audio de-reverberation is the problem of removing reverb from an audio signal without having explicit data regarding the system and/or the input signal. Blind audio de-reverberation is a more difficult signal-processing task than ordinary dereverberation based on deconvolution. In this paper different blind de-reverberation algorithms derived from kurtosis maximization and a maximum likelihood approach are analyzed and implemented.
Download Harmonic Mixing Based on Roughness and Pitch Commonality
The practice of harmonic mixing is a technique used by DJs for the beat-synchronous and harmonic alignment of two or more pieces of music. In this paper, we present a new harmonic mixing method based on psychoacoustic principles. Unlike existing commercial DJ-mixing software which determine compatible matches between songs via key estimation and harmonic relationships in the circle of fifths, our approach is built around the measurement of musical consonance at the signal level. Given two tracks, we first extract a set of partials using a sinusoidal model and average this information over sixteenth note temporal frames. Then within each frame, we measure the consonance between all combinations of dyads according to psychoacoustic models of roughness and pitch commonality. By scaling the partials of one track over ± 6 semitones (in 1/8th semitone steps), we can determine the optimal pitch-shift which maximises the consonance of the resulting mix. Results of a listening test show that the most consonant alignments generated by our method were preferred to those suggested by an existing commercial DJ-mixing system.