基於對抗式適應之跨文化音樂情緒辨識

Abstract

對於建立自動音樂情緒辨識系統而言，收集音樂給人的情緒感受標記是必須的。迄今，大部分的音樂情緒資料集都是以西洋歌曲為主。若音樂情緒辨識系統是以西洋曲風的資料集建立的，此系統可能沒辦法適用於非西洋曲風的歌曲，因這兩個曲風受文化背景的影響，在音樂特徵上以及標記者的情緒感受上皆有不同之處。即使這樣的問題已在跨文化以及跨資料集的研究中被發現，但很少有研究探討如何將用收集到的曲風資料集訓練的模型重新訓練以適應於我們感興趣的曲風上。在本篇論文中，我們提出以非監督式對抗式域適應之方法來解決這個問題。此方法應用了類神經網路之模型使兩曲風學到的表徵無法被區分。又情緒感受本身包含了許多面向，因此我們考慮了與音色、音高、以及節奏性相關之三種輸入特徵來評估模型之成效。結果顯示以西洋流行歌曲訓練的模型透過我們提出的方法可大幅改善用中文歌曲預測情緒正負向的準確率。

Annotation of the perceived emotion of a music piece is needed for an automatic music emotion recognition system. To date, the majority of music emotion datasets are for Western pop songs. A music emotion recognizer trained on such datasets may not work well for non-Western pop songs due to the differences in acoustic characteristics and emotion perception that are inherent to cultural background. Although the problem was also found in cross-cultural and cross-dataset studies, little has been done to learn how to adapt a model pre-trained on a source music genre to a target music genre of interest. In this paper, we propose to address the problem by an unsupervised adversarial domain adaptation method. It employs neural network models to make the target music indistinguishable from the source music in a learned feature representation space. Because emotion perception is multifaceted, three types of input features related to timbre, pitch, and rhythm are considered for performance evaluation. The results show that the proposed method effectively improves the prediction of the valence of Chinese pop songs from a model trained for Western pop songs.