Pedro Vera-Candeas

Transient modeling by matching pursuits with a wavelet dictionary for parametric audio coding

In this letter, we propose a novel matching pursuit-based method for transient modeling with application to parametric audio coding. The overcomplete dictionary for the matching pursuit is composed of wavelet functions that implement a wavelet-packet filter bank. The proposed transient modeling method is suitable to be integrated into a parametric audio coder based on the three-part model of sines, transients, and noise (STN model). Comparative analysis between wavelet and exponentially damped sinusoidal functions are shown in experimental results. The mean-squared-error performance of the proposed approach is better than that obtained with damped sinusoids.

Musical Instrument Sound Multi-Excitation Model for Non-Negative Spectrogram Factorization

This paper presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) to model the excitation of the musical instruments. These excitations represent vibrating objects, while the filter represents the resonance structure of the instrument, which colors the produced sound. We propose to model the excitations as the weighted sum of harmonically constrained basis functions, whose parameters are tied across different pitches of an instrument. An NMF-based framework is used to learn the model parameters. We assume that the excitations of a well-tempered instrument should possess an identifiable characteristic structure whereas the conditions of the music scene might produce variations in the filter. In order to test the reliability of our proposal, we evaluate our method for a music transcription task in two scenarios. On the first one, comparison with state-of-the-art methods has been performed over a dataset of piano recordings obtaining more accurate results than other NMF-based algorithms. On the second one, two woodwind instrument databases have been used to demonstrate the benefits of our model in comparison with previous excitation-filter model approaches.

Adaptive network-based fuzzy inference system vs. other classification algorithms for warped LPC-based speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents an effective approach based on an adaptive network-based fuzzy inference system (ANFIS) for the classification stage required in a speech/music discrimination system. A new simple feature, called warped LPC-based spectral centroid (WLPC-SC), is also proposed. Comparison between WLPC-SC and the classical features proposed in the literature for audio classification is performed, aiming to assess the good discriminatory power of the proposed feature. The vector length used to describe the proposed psychoacoustic-based feature is reduced to a few statistical values (mean, variance and skewness). With the aim of increasing the classification accuracy percentage, the feature space is then transformed to a new feature space by LDA. The classification task is performed applying ANFIS to the features in the transformed space. To evaluate the performance of the ANFIS system for speech/music discrimination, comparison to other commonly used classifiers is reported. The classification results for different types of music and speech signals show the good discriminating power of the proposed approach.

Constrained non-negative sparse coding using learnt instrument templates for realtime music transcription

Abstract In this paper, we present a realtime signal decomposition method with single-pitch and harmonicity constrains using instrument specific information. Although the proposed method is designed for monophonic music transcription, it can be used as a candidate selection technique in combination with other realtime transcription methods to address polyphonic signals. The harmonicity constraint is particularly beneficial for automatic transcription because, in this way, each basis can define a single pitch. Furthermore, restricting the model to have a single-nonzero gain at each frame has been shown to be a very suitable constraint when dealing with monophonic signals. In our method, both harmonicity and single-nonzero gain constraints are enforced in a deterministic manner. A realtime factorization procedure based on Non-negative sparse coding (NNSC) with Beta-divergence and fixed basis functions is proposed. In this paper, the basis functions are learned using a supervised process to obtain spectral patterns for different musical instruments. The proposed method has been tested for music transcription of both monophonic and polyphonic signals and has been compared with other state-of-the-art transcription methods, and in these tests, the proposed method has obtained satisfactory results in terms of accuracy and runtime.

Robust subspace-based fundamental frequency estimation

The problem of fundamental frequency estimation is considered in the context of signals where the frequencies of the harmonics are not exact integer multiples of a fundamental frequency. This frequently occurs in audio signals produced by, for example, stiff-stringed musical instruments, and is sometimes referred to as inharmonicity. We derive a novel robust method based on the subspace orthogonality property of MUSIC and show how it may be used for analyzing audio signals. The proposed method is both more general and less complex than a straight-forward implementation of a parametric model of the inharmonicity derived from a physical instrument model. Additionally, it leads to more accurate estimates of the individual frequencies than the method based on the parametric inharmonicity model and a reduced bias of the fundamental frequency compared to the perfectly harmonic model.

Music Scene-Adaptive Harmonic Dictionary for Unsupervised Note-Event Detection

Harmonic decompositions are a powerful tool dealing with polyphonic music signals in some potential applications such as music visualization, music transcription and instrument recognition. The usefulness of a harmonic decomposition relies on the design of a proper harmonic dictionary. Music scene-adaptive harmonic atoms have been used with this purpose. These atoms are adapted to the musical instruments and to the music scene, including aspects related with the venue, musician, and other relevant acoustic properties. In this paper, an unsupervised process to obtain music scene-adaptive spectral patterns for each MIDI-note is proposed. Furthermore, the obtained harmonic dictionary is applied to note-event detection with matching pursuits. In the case of a music database that only consists of one-instrument signals, promising results (high accuracy and low error rate) have been achieved for note-event detection.

Note-event Detection in Polyphonic Musical Signals based on Harmonic Matching Pursuit and Spectral Smoothness

Abstract Harmonic Matching Pursuit (HMP) is an interesting signal processing tool to detect simultaneous notes in musical audio signals. HMP decomposes an audio signal into harmonic atoms, a suitable choice due to their strong harmonic content. However, HMP provides an inaccurate decomposition when notes with a rational frequency relation are simultaneously played (the overlapping partial problem). An example of such a situation is the common occurrence in Western music of consonant intervals, i.e. perfect fifth (3:2), perfect fourth (4:3) or major third (5:4). For these cases, HMP often obtains harmonic atoms whose fundamental frequencies are the greatest common divisor of the active fundamental frequencies. To solve this problem, we propose a processing algorithm that works over the harmonic atoms obtained by HMP. This algorithm is based on maximizing the smoothness of the spectral envelope for each harmonic atom provided by the HMP decomposition. Results show that our proposal achieves a good performance. Competitive accuracy and error measures in a musical transcription application are reported in relation to the state of the art transcription systems.

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge amount of prior information available in this scenario. In fact, besides the special properties of close-microphone tracks, the knowledge on the number and type of instruments making up the mixture can also be successfully exploited for improved separation performance. In this paper, a nonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a set of instrument models are learnt from a training database and incorporated into a multichannel extension of the NMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple music tracks and comparing the results to other state-of-the-art approaches.

Multiple instrument mixtures source separation evaluation using instrument-dependent NMF models

This work makes use of instrument-dependent models to separate the different sources of multiple instrument mixtures. Three different models are applied: (a) basic spectral model with harmonic constraint, (b) source-filter model with harmonic-comb excitation and (c) source-filter model with multi-excitation per instrument. The parameters of the models are optimized by an augmented NMF algorithm and learnt in a training stage. The models are presented in [1], here the experimental setting for the application to source separation is explained. The instrument-dependent NMF models are first trained and then a test stage is performed. A comparison with other state-of-the-art software is presented. Results show that source-filter model with multi-excitation per instrument outperforms the other compared models.

Online Score-Informed Source Separation with Adaptive Instrument Models

In this paper, an online score-informed source separation system is proposed under the Non-negative Matrix Factorization (NMF) framework, using parametric instrument models. Each instrument is modelled using a multi-excitation source-filter model, which provides the flexibility to model different instruments. The instrument models are initially learned on training excerpts of the same kinds of instruments, and are then adapted, during the separation, to the specific instruments used in the audio being separated. The model adaptation method needs to access the musical score content for each instrument, which is provided by an online audio-score alignment method. Source separation is improved by adapting the instrument models using score alignment. Experiments are performed to evaluate the proposed system and its individual components. Results show that it outperforms a state-of-the-art comparison method.