Paper Archive

This paper proposes a real-time capable method for transcribing and separating occurrences of single drum instruments in polyphonic drum recordings. Both the detection and the decomposition are based on Non-Negative Matrix Factorization and can be implemented with very small systemic delay. We propose a simple modification to the update rules that allows to capture timedynamic spectral characteristics of the involved drum sounds. The method can be applied in music production and music education software. Performance results with respect to drum transcription are presented and discussed. The evaluation data-set consisting of annotated drum recordings is published for use in further studies in the field. Index Terms - drum transcription, source separation, nonnegative matrix factorization, spectral processing, audio plug-in, music production, music education

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In this paper, a novel approach for the computation of a pitch salience function is presented. The aim of a pitch (considered here as synonym for fundamental frequency) salience function is to estimate the relevance of the most salient musical pitches that are present in a certain audio excerpt. Such a function is used in numerous Music Information Retrieval (MIR) tasks such as pitch, multiple-pitch estimation, melody extraction and audio features computation (such as chroma or Pitch Class Profiles). In order to compute the salience of a pitch candidate f , the classical approach uses the weighted sum of the energy of the short time spectrum at its integer multiples frequencies hf . In the present work, we propose a different approach which does not rely on energy but only on frequency location. For this, we first estimate the peaks of the short time spectrum. From the frequency location of these peaks, we evaluate the likelihood that each peak is an harmonic of a given fundamental frequency. The specificity of our method is to use as likelihood the deviation of the harmonic frequency locations from the pitch locations of the equal tempered scale. This is used to create a theoretical sequence of deviations which is then compared to an observed one. The proposed method is then evaluated for a task of multiple-pitch estimation using the MAPS test-set.

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Recently, we have witnessed an increasing use of the sourcefilter model in music analysis, which is achieved by integrating the source filter model into a non-negative matrix factorisation (NMF) framework or statistical models. The combination of the source-filter model and NMF framework reduces the number of free parameters needed and makes the model more flexible to extend. This paper compares four extended source-filter models: the source-filter-decay (SFD) model, the NMF with timefrequency activations (NMF-ARMA) model, the multi-excitation (ME) model and the source-filter model based on β-divergence (SFbeta model). The first two models represent the time-varying spectra by adding a loss filter and a time-varying filter, respectively. The latter two are extended by using multiple excitations and including a scale factor, respectively. The models are tested using sounds of 15 instruments from the RWC Music Database. Performance is evaluated based on the relative reconstruction error. The results show that the NMF-ARMA model outperforms other models, but uses the largest set of parameters.

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Exponentially damped sinusoids (EDS) model-based analysis of sound signals often requires a precise estimation of initial amplitudes and phases of the components found in the sound, on top of a good estimation of their frequencies and damping. This can be of the utmost importance in many applications such as high-quality re-synthesis or identification of structural properties of sound generators (e.g. a physical coupling of vibrating devices). Therefore, in those specific applications, an accurate estimation of the onset time is required. In this paper we present a two-step onset time estimation procedure designed for that purpose. It consists of a “rough" estimation using an STFT-based method followed by a time-domain method to “refine" the previous results. Tests carried out on synthetic signals show that it is possible to estimate onset times with errors as small as 0.2ms. These tests also confirm that operating first in the frequency domain and then in the time domain allows to reach a better resolution vs. speed compromise than using only one frequency-based or one time-based onset detection method. Finally, experiments on real sounds (plucked strings and actual percussions) illustrate how well this method performs in more realistic situations.

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In this paper we present a novel algorithm for automatic analysis, transcription, and parameter extraction from isolated polyphonic guitar recordings. In addition to general score-related information such as note onset, duration, and pitch, instrumentspecific information such as the plucked string, the applied plucking and expression styles are retrieved automatically. For this purpose, we adapted several state-of-the-art approaches for onset and offset detection, multipitch estimation, string estimation, feature extraction, and multi-class classification. Furthermore we investigated a robust partial tracking algorithm with respect to inharmonicity, an extensive extraction of novel and known audio features as well as the exploitation of instrument-based knowledge in the form of plausability filtering to obtain more reliable prediction. Our system achieved very high accuracy values of 98 % for onset and offset detection as well as multipitch estimation. For the instrument-related parameters, the proposed algorithm also showed very good performance with accuracy values of 82 % for the string number, 93 % for the plucking style, and 83 % for the expression style. Index Terms - playing techniques, plucking style, expression style, multiple fundamental frequency estimation, string classification, fretboard position, fingering, electric guitar, inharmonicity coefficient, tablature

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Automatic language identification for singing is a topic that has not received much attention in the past years. Possible application scenarios include searching for musical pieces in a certain language, improvement of similarity search algorithms for music, and improvement of regional music classification and genre classification. It could also serve to mitigate the "glass ceiling" effect. Most existing approaches employ PPRLM processing (Parallel Phone Recognition followed by Language Modeling). We present a new approach for singing language identification. PLP, MFCC, and SDC features are extracted from audio files and then passed through an i-vector extractor. This algorithm reduces the training data for each sample to a single 450-dimensional feature vector. We then train Neural Networks and Support Vector Machines on these feature vectors. Due to the reduced data, the training process is very fast. The results are comparable to the state of the art, reaching accuracies of 83% on a large speech corpus and 78% on acapella singing. In contrast to PPRLM approaches, our algorithm does not require phoneme-wise annotations and is easier to implement.

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In this work we present a new scenario of analyzing and separating linear mixtures of musical instrument signals. When instruments are playing in unison, traditional source separation methods are not performing well. Although the sources share the same pitch, they often still differ in their modulation frequency caused by vibrato and/or tremolo effects. In this paper we propose source separation schemes that exploit AM/FM characteristics to improve the separation quality of such mixtures. We show a method to process mixtures based on differences in their amplitude modulation frequency of the sources by using non-negative tensor factorization. Further, we propose an informed warped time domain approach for separating mixtures based on variations in the instantaneous frequencies of the sources.

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An algorithm for estimating the fundamental frequency of a singlepitch audio signal is described, for application to audio-to-MIDI conversion. In order to minimize latency, this method is based on the ESPRIT algorithm, together with a statistical model for partials frequencies. It is tested on real guitar recordings and compared to the YIN estimator. We show that, in this particular context, both methods exhibit a similar accuracy but the periodicity measure, used for note segmentation, is much more stable with the ESPRITbased algorithm. This allows to significantly reduce ghost notes. This method is also able to get very close to the theoretical minimum latency, i.e. the fundamental period of the lowest observable pitch. Furthermore, it appears that fast implementations can reach a reasonable complexity and could be compatible with real-time, although this is not tested is this study.

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Time-scale modification (TSM) algorithms have the purpose of stretching or compressing the time-scale of an input audio signal without altering its pitch. Such tools are frequently used in scenarios like music production or music remixing. There exists a large variety of different algorithmic approaches to TSM, all of them having their very own advantages and drawbacks. In this paper, we present the TSM toolbox, which contains MATLAB implementations of several conceptually different TSM algorithms. In particular, our toolbox provides the code for a recently proposed TSM approach, which integrates different classical TSM algorithms in combination with harmonic-percussive source separation (HPSS). Furthermore, our toolbox contains several demo applications and additional code examples. Providing MATLAB code on a well-documented website under a GNU-GPL license and including illustrative examples, our aim is to foster research and education in the field of audio processing.

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In this paper, the development and application of a universal audio-DSP (digital signal processor) board will be described. It is called freeDSP. Our goal was to provide an affordable real-time signal processing solution for researchers and the do-it-yourself community. Easy assembling and simple programmability were the main focus. A solution based on Analog Devices’ ADAU1701 DSP chip together with the free graphical development environment SigmaStudio is proposed. The applications range from active loudspeaker compensation over steerable microphone arrays to advanced audio effect processors. The freeDSP board is published under a creative commons license, which allows the unrestricted use and modification of the module.

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