Scalability of particle swarm algorithms

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An exploration of genetic algorithms for efficient musical instrument identification

This study explores the use of genetic algorithms (GA) in optimising feature selection for musical instrument recognition. 95 timbral features were used to classify 3006 musical instrument samples into 5 instrument groups. A GA was used to optimise the best selection of features to use with an multi-layered perceptron (MLP) to classify the instruments. Of all the features examined, the Centroid Evolution was found to be the most important. The system was run a number of times with varying numbers of features as determined by the GA. The accuracy of the classi er was not reduced with a reduction in features, indicating that the GA successfully determined the best features to use

A parametric model for spectral sound synthesis of musical sounds

We introduce a reduced parameter synthesis model for the spectral synthesis of musical sounds, which preserves the timbre and the naturalness of the musical sound. It also provides large flexibility for the user and reduces the number of synthesis parameters compared to traditional analysis/re-synthesis methods. The proposed model is almost completely independent from a previous spectral analysis. We present a frequency estimation method using a random walk to keep the naturalness of the sound without using a separate noise model. Three different approaches have been tested to estimate the amplitude values for the synthesis, namely, local optimization, the use of a lowpass filter and polynomial fitting. All of these approaches give good results, especially for the sustain part of the signal

Time domain attack and release modeling - applied to spectral domain sound synthesis

We introduce a time-domain model for the synthesis of attack and release parts of musical sounds. This approach is an extension of a spectral synthesis model we developed: the Parametric Synthesis Model (PSM). The attack and release model is independent from a preceeding spectral analysis as it is based on the time domain sustain part of the sound. We apply a shaping function to this sustain part to obtain the sound attack and the release. The model has been tested with linear and polynomial shaping functions and produces good results for three different instruments. The time-domain approach also overcomes the problem of synthesis artifacts that often occur when using spectral analysis/synthesis methods for sounds with transient events. Moreover, the model can be combined with any synthesis model of the sustain part and offers the possibility to determine the duration of the attack and release parts of the sound

On the scalability of particle swarm optimisation

Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some function

The use of mel-frequency cepstral coefficients in musical instrument identification

This paper examines the use of Mel-frequency Cepstral Coefficients in the classification of musical instruments. 2004 piano, violin and flute samples are analysed to get their coefficients. These coefficients are reduced using principal component analysis and used to train a multi-layered perceptron. The network is trained on the first 3, 4 and 5 principal components calculated from the envelope of the changes in the coefficients. This trained network is then used to classify novel input samples. By training and testing the network on a different number of coefficients, the optimum number of coefficients to include for identifying a musical instrument is determined. We conclude that using 4 principal components from the first 15 coefficients gives the most accurate classification results

Genetic programming for musical sound analysis

This study uses Genetic Programming (GP) in developing a classi er to distinguish between ve musical instruments. Using only simple arithmetic and boolean operators with 95 features as terminals, a program is developed that can classify 300 unseen samples with an accuracy of 94%. The experiment is then run again using only 14 of the most often chosen features. Limiting the features in this way raised the best classi cation to 94.3% and the average accuracy from 68.2% to 75.67%. This demonstrates that not only can GP be used to create a classi er but it can be used to determine the best features to choose for accurate musical instrument classi cation, giving an insight into timbr

Musical instrument identification using principal componant analysis and multi-layered perceptions

This study aims to create an automatic musical instrument classifier by extracting audio features from real sample sounds. These features are reduced using Principal Component Analysis and the resultant data is used to train a Multi-Layered Perceptron. We found that the RMS temporal envelope and the evolution of the centroid gave the most interesting results of the features studied. These results were found to be competitive whether the scope of the data was across one octave or across the range of each instrumen

Comparison of features in musical instrument identification using artificial neural networks

This paper examines the use of a number of auditory features in identifying musical instruments. The Temporal Envelope, Centroid, Melfrequency Cepstral Coefficients (MFCCs), Inharmonicity, Spectral Irregularity and Number of Spectral Peaks are all examined. By using these features to train a Multi-Layered Perceptron (MLP), it is determined that the MFCCs are the most efficient of these features in musical instrument identification. The Inharmonicity, Spectral Irregularity and Number of Spectral Peaks offered no benefit to the classifier. Of the instruments studied, the piano was most accurately classified and the violin was the least accurately classified instrument