Antonio Pena

Reliability-Informed Beat Tracking of Musical Signals

A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k -nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping.

Onset Event Decoding Exploiting the Rhythmic Structure of Polyphonic Music

In this paper, we propose a rhythmically informed method for onset detection in polyphonic music. Music is highly structured in terms of the temporal regularity underlying onset occurrences and this rhythmic structure can be used to locate sound events. Using a probabilistic formulation, the method integrates information extracted from the audio signal and rhythmic knowledge derived from tempo estimates in order to exploit the temporal expectations associated with rhythm and make musically meaningful event detections. To do so, the system explicitly models note events in terms of the elapsed time between consecutive events and decodes the most likely sequence of onsets that led to the observed audio signal. In this way, the proposed method is able to identify likely time instants for onsets and to successfully exploit the temporal regularity of music. The goal of this work is to define a general framework to be used in combination with any onset detection function and tempo estimator. The method is evaluated using a dataset of music that contains multiple instruments playing at the same time, including singing and different music genres. Results show that the use of rhythmic information improves the commonly used adaptive thresholding onset detection method which only considers local information. It is also shown that the proposed probabilistic framework successfully exploits rhythmic information using different detection functions and tempo estimation algorithms.

Note onset detection using rhythmic structure

In this paper we explore the relationship between the temporal and rhythmic structure of musical audio signals. Using automatically extracted rhythmic structure we present a rhythmically-aware method to combine note onset detection techniques. Our method uses topdown knowledge of repetitions of musical events to improve detection performance by modelling the temporal distribution of onset locations. Results on a publicly available database demonstrate that using musical knowledge in this way can lead to significant improvements by reducing the number of missed and spurious detections.

A fast noise-scaling algorithm for uniform quantization in audio coding schemes

A new bit assignment algorithm is presented. Its goals are the simultaneous assignment on all subbands in a few steps of an iterative calculus, the use of memory to achieve a better speed of convergence and the consideration of a deformable error curve. The basis of the algorithm is discussed and also other considerations that are likely to arise in practice. Finally, an example of its performance is given.

An adaptive tree search algorithm with application to multiresolution based perceptive audio coding

Search algorithms for selecting signal decompositions based on the minimization of a cost functional have been proposed in the literature, in addition to several additive and non-additive information costs. We introduce a new cost function and a search algorithm built from a perceptual criterion. Their efficiency is demonstrated with results showing higher quality compressed audio signals than preliminary approaches at similar bit-rates.

Low-complexity bit-allocation algorithm for MPEG AAC audio coders

This letter presents a method for estimating the quantization noise introduced by a nonuniform quantizer, like those used in the family of MPEG-2/4 AAC audio coders. The method is generalized for the case of estimating the mean squared or the maximum value of the quantization noise. Its use will allow the bit-allocation algorithm to be adapted to different coding scenarios, depending on the available number of bits. Using the proposed method, it is possible to implement a loopless bit-allocation algorithm, without the need for using any kind of iteration loops. This helps to dramatically reduce the computational complexity of the bit-allocation algorithm, making it easier to implement in real-time applications where computational power is limited.

A flexible tiling of the time axis for adaptive wavelet packet decompositions

A segmentation procedure of time sequences based on a time-frequency analysis is presented. The use of both a wavelet packet transform and the original time signal provides a set of spectral and time parameters that allows the algorithm to locate some proper break points to split the input frame into a discrete number of smaller segments. Some examples showing the performance of the method are also presented. An application to wavelet-based audio coding is also discussed.