Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Application of Temporal Descriptors to Musical Instrument Sound Recognition
Journal of Intelligent Information Systems
Automatic recognition of isolated monophonic musical instrument sounds using kNNC
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Musical Sound Classification based on Wavelet Analysis
Fundamenta Informaticae - Intelligent Systems
Musical instrument timbres classification with spectral features
EURASIP Journal on Applied Signal Processing
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
Multiclass MTS for Simultaneous Feature Selection and Classification
IEEE Transactions on Knowledge and Data Engineering
Musical instrument recognition by pairwise classification strategies
IEEE Transactions on Audio, Speech, and Language Processing
Automated classification of piano-guitar notes
IEEE Transactions on Audio, Speech, and Language Processing
Pattern Recognition Approach for Music Style Identification Using Shallow Statistical Descriptors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Study on Feature Analysis for Musical Instrument Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Performance-Based Interpreter Identification in Saxophone Audio Recordings
IEEE Transactions on Circuits and Systems for Video Technology
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Under the highly developed automation today, the manufacture of saxophone is still a nonautomatic process and much relies on highly skilled technicians. In order to insure the timbre quality, the sound of finished saxophone must be tested in the final inspection stage. The evaluation of timbre quality mainly depends on the professional musicians' hearing judgment; however, the sensitivity of human perception can be influenced by many factors. To improve the reliability of saxophone timbre quality inspection, an automatic multiclass timbre classification system (AMTCS) is developed and used to assist in the inspection work. The AMTCS is composed of our proposed waveform-shape-based feature extraction method in parameterization phase and multiclass Mahalanobis-Taguchi system in classification phase. The numerical experiments show that the musical instrument classification accuracy obtained by our proposed AMTCS is satisfactory. Through employing the AMTCS, strong assistance was provided to the inspection of saxophone timbre quality, and a perfect identification rate on the saxophones with different timbre quality levels is achieved. Moreover, the significant tones having impact on saxophone timbre quality can also be easily identified by AMTCS.