Improving piano music signal recognition through enhanced frequency domain analysis

Abstract

Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis.