Musical Instrument Categorization using Spectral- and Cepstral Analysis
(2016) MASY01 20161Mathematical Statistics
- Abstract
- This thesis suggests and tests two methods for categorizing instruments in a musical signal. The
first method involves spectral analysis and the magnitude spectrum, analysing the structure of
pitches and overtones and how the amplitude of those changes over time. The second method involves
cepstrum analysis and especially the, for speech recognition popular method computing the
Mel Frequency Cepstral Coefficients (MFCC). For each method, a weighting procedure using Least
Squares is finally used to determine which instrument that are present in a signal and which are
not. This is done by comparing the unknown signal to a reference bank of tones. So, even if this
thesis is primarily about testing and comparing two methods, it is also... (More) - This thesis suggests and tests two methods for categorizing instruments in a musical signal. The
first method involves spectral analysis and the magnitude spectrum, analysing the structure of
pitches and overtones and how the amplitude of those changes over time. The second method involves
cepstrum analysis and especially the, for speech recognition popular method computing the
Mel Frequency Cepstral Coefficients (MFCC). For each method, a weighting procedure using Least
Squares is finally used to determine which instrument that are present in a signal and which are
not. This is done by comparing the unknown signal to a reference bank of tones. So, even if this
thesis is primarily about testing and comparing two methods, it is also about how much a linear
Least Squares weighting method can absorb the non-linear calculations of magnitude spectrum and
MFCC.
The results for both methods in combination with the Least Squares weighting procedure are
promising. For some musical signals the instrument categorisation succeeds very well while for
other signals improvements of the methods are needed. These improvements may be achieved by
combining features from both methods and adjusting the weighting system. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8891409
- author
- Florén, Jakob and Torby, Joel
- supervisor
- organization
- course
- MASY01 20161
- year
- 2016
- type
- M2 - Bachelor Degree
- subject
- language
- English
- id
- 8891409
- date added to LUP
- 2016-09-12 13:35:47
- date last changed
- 2016-09-12 13:35:47
@misc{8891409, abstract = {{This thesis suggests and tests two methods for categorizing instruments in a musical signal. The first method involves spectral analysis and the magnitude spectrum, analysing the structure of pitches and overtones and how the amplitude of those changes over time. The second method involves cepstrum analysis and especially the, for speech recognition popular method computing the Mel Frequency Cepstral Coefficients (MFCC). For each method, a weighting procedure using Least Squares is finally used to determine which instrument that are present in a signal and which are not. This is done by comparing the unknown signal to a reference bank of tones. So, even if this thesis is primarily about testing and comparing two methods, it is also about how much a linear Least Squares weighting method can absorb the non-linear calculations of magnitude spectrum and MFCC. The results for both methods in combination with the Least Squares weighting procedure are promising. For some musical signals the instrument categorisation succeeds very well while for other signals improvements of the methods are needed. These improvements may be achieved by combining features from both methods and adjusting the weighting system.}}, author = {{Florén, Jakob and Torby, Joel}}, language = {{eng}}, note = {{Student Paper}}, title = {{Musical Instrument Categorization using Spectral- and Cepstral Analysis}}, year = {{2016}}, }