Research on Violin Audio Feature Recognition Based on Mel-frequency Cepstral Coefficient-based Feature Parameter Extraction

This paper focuses on the feature recognition of violin audio. After introducing the preprocessing method, the common feature parameters, linear predictive cepstral coefficient (LPCC) and mel-frequency cepstral coefficient (MFCC), were explained. Then, MFCC + △MFCC was used as the feature parameter. The parameters of support vector machine (SVM) were optimized using the firefly algorithm (FA). The FA-SVM method was used to recognize different violin audios. It was found that the identification rate of the FA-SVM approach was above 95% for different violin notes. The recognition effect was better when using MFCC + △MFCC as the feature parameter compared with LPCC and MFCC. The FA-SVM method achieved the highest recognition rate of 97.42%. The results demonstrate the reliability of the FA-SVM method based on MFCC feature parameter extraction. This method can be applied in practical audio recognition.