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

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

Blood detection in the spinal column of whole cooked chicken using an optical fibre based sensor system

An optical fibre based sensor has been developed to aid the quality assurance of food cooked in industrial ovens by monitoring the product in situ as it cooks. The sensor measures the product colour as it cooks by examining the reflected visible light from the surface as well as the core of the product. This paper examines the use of the sensor for the detection of blood in the spinal area of cooked whole chickens. The results presented here show that the sensor can be successfully used for this purpose

Detection of premature browning in ground beef with an integrated optical-fibre based sensor using reflection spectroscopy and fibre Bragg grating technology

This paper reports on an optical fibre based sensor system to detect the occurrence of premature browning in ground beef. Premature browning (PMB) occurs when, at a temperature below the pasteurisation temperature of 71°C, there are no traces of pink meat left in the patty. PMB is more frequent if poorer quality beef or beef that has been stored under imperfect conditions. The experimental work pertaining to this paper involved cooking fresh meat and meat that has been stored in a freezer for, 1 week, 1 month and 3 months and recording the reflected spectra and temperature at the core of the product, during the cooking process, in order to develop a classifier based on the spectral response and using a Self-Organising Map (SOM) to classify the patties into one of four categories, based on their colour. Further tests were also carried out on developing an all-optical fibre sensor for measuring both the temperature and colour in a single integrated probe. The integrated probe contains two different sensor concepts, one to monitor temperature, based on Fibre Bragg Grating (FBG) technology and a second for meat quality, based on reflection spectroscopy in the visible wavelength range