9 research outputs found
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