A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm

Abstract

In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.

Comparative Study of Musical Timbral Variations: Crescendo and Vibrato Using FFT-Acoustic Descriptor

“…The problems of classification can be resolved using supervised learning. These classification algorithms have been used in music style recognition problems through music feature extraction [12], musical instrument classification problems [13], and the use of an intelligent system for piano timbre recognition [14], among other techniques. We are going to compare the classification capacities of the timbral coefficients proposed by González and Prati [8] with some timbral features extracted using Librosa: Chroma stft, spectral contrast, spectral flatness, poly features, spectral centroid, spectral rolloff, and spectral bandwidth [15].…”

Ambient Sound Recognition using Convolutional Neural Networks

“…However, we have used Support Vector Machine, SVM classifier for comparison to the accuracy results that will be obtained using CNN. Also, we are going to compare different neural networks used for sound recognition and classification like Recurrent Neural Network, RNN in [6][7][8] and k-Nearest Neighbor, KNN in [9][10][11][12] to the ones that have used CNN and SVM classifiers like in this project to analyze their differences and their respective performances in achieving the task of sound recognition. This will also be accompanied by the comparison of CNN and SVM classifiers to perform the same task.…”