Auditory Spectrum-Based Pitched Instrument Onset Detection

In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitch-based features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%

Microtiming patterns and interactions with musical properties in Samba music

In this study, we focus on the interaction between microtiming patterns and several musical properties: intensity, meter and spectral characteristics. The data-set of 106 musical audio excerpts is processed by means of an auditory model and then divided into several spectral regions and metric levels. The resulting segments are described in terms of their musical properties, over which patterns of peak positions and their intensities are sought. A clustering algorithm is used to systematize the process of pattern detection. The results confirm previously reported anticipations of the third and fourth semiquavers in a beat. We also argue that these patterns of microtiming deviations interact with different profiles of intensities that change according to the metrical structure and spectral characteristics. In particular, we suggest two new findings: (i) a small delay of microtiming positions at the lower end of the spectrum on the first semiquaver of each beat and (ii) systematic forms of accelerando and ritardando at a microtiming level covering two-beat and four-beat phrases. The results demonstrate the importance of multidimensional interactions with timing aspects of music. However, more research is needed in order to find proper representations for rhythm and microtiming aspects in such contexts

Automating Ornamentation Transcription

A novel technique for detecting single and multi-note ornaments is presented. The system detects audio segments by utilising and onset detector based on comb filters (ODCF), which is capable of detecting very close events. In addition, a novel method to remove spurious onsets due to offset events is introduced. The system utilises musical ornamentation theory to decide whether a sequence of audio segments correspond to an ornamentation musical structure. In order to evaluate the results, a database of signals produced by different players using the three different instruments has been utilised. The results represent a step forward towards fully automating ornamentation transcriptio

Gamelan Music Onset Detection based on Spectral Features

This research detects onsets of percussive instruments by examining the performance on the sound signals of gamelan instruments as one of  traditional music instruments in Indonesia. Onset plays important role in determining musical rythmic structure, like beat, tempo, measure, and is highly required in many applications of music information retrieval. Four onset detection methods that employ spectral features, such as magnitude, phase, and the combination of both are compared in this paper. They are phase slope (PS), weighted phase deviation (WPD), spectral flux (SF), and rectified complex domain (RCD). Features are extracted by representing the sound signals into time-frequency domain using overlapped Short-time Fourier Transform (STFT) and by varying the window length. Onset detection functions are processed through peak-picking using dynamic threshold. The results showed that by using suitable window length and parameter setting of dynamic threshold, F-measure which is greater than 0.80 can be obtained for certain methods

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques

 In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%

Biologically-inspired neural coding of sound onset for a musical sound classification task

A biologically-inspired neural coding scheme for the early auditory system is outlined. The cochlea response is simulated with a passive gammatone filterbank. The output of each bandpass filter is spike-encoded using a zero-crossing based method over a range of sensitivity levels. The scheme is inspired by the highly parallellised nature of the auditory nerve innervation within the cochlea. A key aspect of early auditory processing is simulated, namely that of onset detection, using leaky integrate-and-fire neuron models. Finally, a time-domain neural network (the echo state network) is used to tackle the what task of auditory perception using the output of the onset detection neuron alone