Francisco J. Rodríguez-Serrano

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.

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.

Parallel online time warping for real-time audio-to-score alignment in multi-core systems

The audio-to-score framework consists of two separate stages: preprocessing and alignment. The alignment is commonly solved through offline dynamic time warping (DTW), which is a method to find the path over the distortion matrix with the minimum cost to determine the relation between the performance and the musical score times. In this work we propose a parallel online DTW solution based on a client–server architecture. The current version of the application has been implemented for multi-core architectures (

Tempo Driven Audio-to-Score Alignment Using Spectral Decomposition and Online Dynamic Time Warping

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Voicing detection based on adaptive aperiodicity thresholding for speech enhancement in non-stationary noise

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