Music Tempo Estimation With $k$-NN Regression

An approach for tempo estimation from musical pieces with near-constant tempo is proposed. The method consists of three main steps: measuring the degree of musical accent as a function of time, periodicity analysis, and tempo estimation. Novel accent features based on the chroma representation are proposed. The periodicity of the accent signal is measured using the generalized autocorrelation function, followed by tempo estimation using k-Nearest Neighbor regression. We propose a resampling step applied to an unknown periodicity vector before finding the nearest neighbors. This step improves the performance of the method significantly. The tempo estimate is computed as a distance-weighted median of the nearest neighbor tempi. Experimental results show that the proposed method provides significantly better tempo estimation accuracies than three reference methods.     

Parallel realizations of Kanerva's sparse distributed memory on a tree-shaped computer

This paper presents two parallel realizations of sparse distributed memory (SDM) on a tree-shaped computer. The original model of SDM is introduced in terms of generalized computer memory and artificial neural networks (ANNs). For parallellization purposes, addressing, storage and retrieval operations are explained in detail. Some existing implementations in various computing platforms are considered before introducing the tree-shaped parallel computer, TUTNC (Tampere University of Technology Neural Computer). Two mappings are given, each utilizing parallelism with different granularities, and compared in terms of measured execution time, task partitioning and load balancing. Performance estimates are given for a larger system. The results show that SDM can be well parallelized in TUTNC. © 1997 John Wiley & Sons, Ltd.     

Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model

A method is described for estimating the fundamental frequencies of several concurrent sounds in polyphonic music and multiple-speaker speech signals. The method consists of a computational model of the human auditory periphery, followed by a periodicity analysis mechanism where fundamental frequencies are iteratively detected and canceled from the mixture signal. The auditory model needs to be computed only once, and a computationally efficient strategy is proposed for implementing it. Simulation experiments were made using mixtures of musical sounds and mixed speech utterances. The proposed method outperformed two reference methods in the evaluations and showed a high level of robustness in processing signals where important parts of the audible spectrum were deleted to simulate bandlimited interference. Different system configurations were studied to identify the conditions where pitch analysis using an auditory model is advantageous over conventional time or frequency domain approaches.