Singer identification is a challenging task in music information retrieval because of the combined instrumental music with the singing voice. The previous approaches focus on identification of singers based on individual features extracted from the music clips. The objective of this work is to combine Mel Frequency Cepstral Coefficients (MFCC) and Chroma DCT-reduced Pitch (CRP) features for singer identification system (SID) using machine learning techniques. The proposed system has mainly two phases. In the feature extraction phase, MFCC, [Formula: see text]MFCC, [Formula: see text]MFCC and CRP features are extracted from the music clips. In the identification phase, extracted features are trained with Bidirectional Long Short-Term Memory (BLSTM)-based Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN) and tested to identify different singer classes. The identification accuracy and Equal Error Rate (EER) are used as performance measures. Further, the experiments also demonstrate the effectiveness of score level fusion of MFCC and CRP feature in the singer identification system. Also, the experimental results are compared with the baseline system using support vector machines (SVM).

中文翻译：

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使用机器学习算法进行歌手识别的 MFCC 和 CRP 特征的组合证据

由于器乐与歌声相结合，歌手识别是音乐信息检索中的一项具有挑战性的任务。以前的方法侧重于基于从音乐剪辑中提取的个人特征来识别歌手。这项工作的目的是结合梅尔频率倒谱系数 (MFCC) 和色度 DCT 降低音高 (CRP) 特征，用于使用机器学习技术的歌手识别系统 (SID)。建议的系统主要有两个阶段。在特征提取阶段，从音乐片段中提取MFCC、[公式：见文]MFCC、[公式：见文]MFCC和CRP特征。在识别阶段，提取的特征使用基于双向长短期记忆 (BLSTM) 的递归神经网络 (RNN) 和卷积神经网络 (CNN) 进行训练，并进行测试以识别不同的歌手类别。识别精度和等误码率（EER）被用作性能指标。此外，实验还证明了歌手识别系统中MFCC和CRP特征的得分水平融合的有效性。此外，实验结果与使用支持向量机（SVM）的基线系统进行了比较。