埃塞俄比亚圣歌、阿兹马里与当代音乐中的Kinit分类：新数据集与CNN基准
Kinit Classification in Ethiopian Chants, Azmaris and Modern Music: A New Dataset and CNN Benchmark
AI总结：
本文创建首个埃塞俄比亚音乐数据集EMIR，提出基于VGG的EKM模型进行Kinit分类，实验表明MFCC特征和3秒样本最优，EKM准确率达95.00%且训练最快。
摘要：
在本文中，我们创建了EMIR，这是首个针对埃塞俄比亚音乐的音乐信息检索数据集。EMIR可免费用于研究目的，包含600个样本录音，涵盖东正教特瓦赫多圣歌、传统阿兹马里歌曲和当代埃塞俄比亚世俗音乐。每个样本由五位专家评审员分类为四种著名的埃塞俄比亚Kinit之一：Tizita、Bati、Ambassel和Anchihoye。每种Kinit使用各自的五声音阶，并具有自身的风格特征。因此，Kinit分类需要将音阶识别与流派识别相结合。在描述数据集之后，我们提出了基于VGG的Ethio Kinits模型（EKM），用于对EMIR片段进行分类。在实验1中，我们研究了Filterbank、Mel-spectrogram、Chroma或Mel频率倒谱系数（MFCC）特征中哪一种最适合使用EKM进行Kinit分类。结果发现MFCC更优，因此在实验2中采用，其中使用三种不同的音频样本长度比较了使用MFCC的EKM模型的性能。3秒长度给出了最佳结果。在实验3中，我们在EMIR数据集上比较了EKM与四种现有模型：AlexNet、ResNet50、VGG16和LSTM。发现EKM具有最佳准确率（95.00%）以及最快的训练时间。我们希望这项工作能鼓励其他人探索埃塞俄比亚音乐，并尝试其他模型进行Kinit分类。
英文摘要：
In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kinits, Tizita, Bati, Ambassel and Anchihoye. Each Kinit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kinit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kinits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kinit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kinit classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
