Paper Archive

Various methods have been proposed for detecting the onset times of musical notes in audio signals. We examine recent work on onset detection using spectral features such as the magnitude, phase and complex domain representations, and propose improvements to these methods: a weighted phase deviation function and a halfwave rectified complex difference. These new algorithms are compared with several state-of-the-art algorithms from the literature, and these are tested using a standard data set of short excerpts from a range of instruments (1060 onsets), plus a much larger data set of piano music (106054 onsets). Some of the results contradict previously published results and suggest that a similarly high level of performance can be obtained with a magnitude-based (spectral flux), a phase-based (weighted phase deviation) or a complex domain (complex difference) onset detection function.

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This paper introduces a new open-source Python library for the modeling and simulation of wave digital filter (WDF) circuits. The library, called pwydf, allows users to easily create and analyze WDF circuit models in a high-level, object-oriented manner. The library includes a variety of built-in components, such as voltage sources, capacitors, diodes etc., as well as the ability to create custom components and circuits. Additionally, pywdf includes a variety of analysis tools, such as frequency response and transient analysis, to aid in the design and optimization of WDF circuits. We demonstrate the library’s efficacy in replicating the nonlinear behavior of an analog diode clipper circuit, and in creating an allpass filter that cannot be realized in the analog world. The library is well-documented and includes several examples to help users get started. Overall, pywdf is a powerful tool for anyone working with WDF circuits, and we hope it can be of great use to researchers and engineers in the field.

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This paper presents some possible relationships between gesture and sound that may be built with an interactive digital audio environment. In a traditional musical situation gesture usually produces sound. The relationship between gesture and sound is unique, it is a cause to effect link. In computer music, the possibility of uncoupling gesture from sound is due to the fact that computer can carry out all the aspects of sound production from composition up to interpretation and performance. Real time computing technology and development of human gesture tracking systems may enable gesture to be introduced again into the practice of computer music, but with a completely renewed approach. There is no more need to create direct cause to effect relationships for sound production, and gesture may be seen as another musical parameter to play with in the context of interactive musical performances.

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A FPGA-based system suitable for augmented reality audio applications is presented. The sample application described here is adaptive noise cancellation (ANC). The system consists of a Spartan -3 FPGA XC3S400 board connected to a Philips Stereo-AudioCodec UCB 1400. The algorithms for the FIR filtering and for the adaption of the filter coefficients according to the Widrow-Hoff LMS algorithm are implemented on the FPGA board. Measurement results obtained with a dummy head measuring system are reported, and a detailed analysis of system performance and possible system improvements is given.

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We propose a new statistical model of musical timbre that handles the different segments of the temporal envelope (attack, sustain and release) separately in order to account for their different spectral and temporal behaviors. The model is based on a reduced-dimensionality representation of the spectro-temporal envelope. Temporal coefficients corresponding to the attack and release segments are subjected to explicit trajectory modeling based on a non-stationary Gaussian Process. Coefficients corresponding to the sustain phase are modeled as a multivariate Gaussian. A compound similarity measure associated with the segmental model is proposed and successfully tested in instrument classification experiments. Apart from its use in a statistical framework, the modeling method allows intuitive and informative visualizations of the characteristics of musical timbre.

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In binaural signals, interaural time differences (ITDs) and interaural level differences (ILDs) are two of the most important cues for the estimation of source azimuths, i.e. the localization of sources in the horizontal plane. For narrow band signals, according to the duplex theory, ITD is dominant at low frequencies and ILD is dominant at higher frequencies. Based on the STFT spectra of binaural signals, a method is proposed for the combined evaluation of ITD and ILD for each individual spectral coefficient. ITD and ILD are related to the azimuth through lookup models. Azimuth estimates based on ITD are more accurate but ambiguous at higher frequencies due to phase wrapping. The less accurate but unambiguous azimuth estimates based on ILDs are used in order to select the closest candidate azimuth estimates based on ITDs, effectively improving the azimuth estimation. The method corresponds well with the duplex theory and also handles the transition from low to high frequencies gracefully. The relations between the ITD and ILD and the azimuth are computed from a measured set of head related transfer functions (HRTFs), yielding azimuth lookup models. Based on a study of these models for different subjects, parametric azimuth lookup models are proposed. The parameters of these models can be optimized for an individual subject whose HRTFs have been measured. In addition, subject independent lookup models are proposed, parametrized only by the distance between the ears, effectively enabling source localization for subjects whose HRTFs have not been measured.

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This study evaluates the performance of five objective audio quality metrics—PEAQ Basic, PEAQ Advanced, PEMO-Q, ViSQOL, and HAAQI —in the context of digital music production. Unlike previous comparisons, we focus on their suitability for production environments, an area currently underexplored in existing research. Twelve audio examples were tested using two evaluation types: an effectiveness test under progressively increasing degradations (hum, hiss, clipping, glitches) and a robustness test under fixed-level, randomly fluctuating degradations. In the effectiveness test, HAAQI, PEMO-Q, and PEAQ Basic effectively tracked degradation changes, while PEAQ Advanced failed consistently and ViSQOL showed low sensitivity to hum and glitches. In the robustness test, ViSQOL and HAAQI demonstrated the highest consistency, with average standard deviations of 0.004 and 0.007, respectively, followed by PEMO-Q (0.021), PEAQ Basic (0.057), and PEAQ Advanced (0.065). However, ViSQOL also showed low variability across audio examples, suggesting limited genre sensitivity. These findings highlight the strengths and limitations of each metric for music production, specifically quality measurement with compressed audio. The source code and dataset will be made publicly available upon publication.

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The relation between anthropometric parameters and Head-Related Transfer Function (HRTF) features, especially those due to the pinna, are not fully understood yet. In this paper we apply signal processing techniques to extract the frequencies of the main pinna notches (known as N1 , N2 , and N3 ) in the frontal part of the median plane and build a model relating them to 13 different anthropometric parameters of the pinna, some of which depend on the elevation angle of the sound source. Results show that while the considered anthropometric parameters are not able to approximate with sufficient accuracy neither the N2 nor the N3 frequency, eight of them are sufficient for modeling the frequency of N1 within a psychoacoustically acceptable margin of error. In particular, distances between the ear canal and the outer helix border are the most important parameters for predicting N1 .

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We present MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches – including new strategies we have developed –, that users can select and parametrize. This paper offers an overview of the set of features, related, among others, to timbre, tonality, rhythm or form, that can be extracted with MIRtoolbox. Four particular analyses are provided as examples. The toolbox also includes functions for statistical analysis, segmentation and clustering. Particular attention has been paid to the design of a syntax that offers both simplicity of use and transparent adaptiveness to a multiplicity of possible input types. Each feature extraction method can accept as argument an audio file, or any preliminary result from intermediary stages of the chain of operations. Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multichannel signals, etc. For that purpose, the data and methods of the toolbox are organised in an object-oriented architecture.

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In monaural blind audio source separation scenarios, a signal mixture is usually separated into more signals than active sources. Therefore it is necessary to group the separated signals to the final source estimations. Traditionally grouping methods are supervised and thus need a learning step on appropriate training data. In contrast, we discuss unsupervised clustering of the separated channels by Mel frequency cepstrum coefficients (MFCC). We show that replacing the decorrelation step of the MFCC by the non-negative matrix factorization improves the separation quality significantly. The algorithms have been evaluated on a large test set consisting of melodies played with different instruments, vocals, speech, and noise.

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