音樂情緒辨識(Music Emotion Recognition - MER)分析了音樂與人之間的關係，音樂情緒辨識在音樂理解、音樂檢索等其他相關應用是有幫助的。隨著音樂數量的增加，透過情緒來挑選音樂的需求也跟著出現。音樂情緒辨識需要考慮到音樂心理學的特性，雖然音樂情緒辨識已經發展了一段時間，但仍未有一個非常準確的音樂情緒辨識系統。

在本論文中，我們提出基於兩種音樂格式的音樂情緒辨識系統，兩個音樂格式分別使用不同的機器學習模型，本系統包括了數位訊號音樂情緒辨識、MIDI音樂情緒辨識及決策模型三個部份；在數位訊號擷取WAVE的37個特徵，並透過RreliefF演算法計算每一個特徵權重，並根據權重的高低依序帶入支持向量機(Support Vector Machine, SVM)分類情緒，並觀察辨識結果；MIDI音樂情緒辨識部份的特徵則包括單位時間特徵、全域特徵及樂器特徵，其使用的機器學習模型為深度信念網路(Deep Belief Network, DBN)；最後，我們使用正規化代數乘法(Normalized Algebraic Product, NAP)整合上述兩者的辨識結果。

Music emotion recognition (MER) detects and analyzes the relation between human emotion and music clips. MER is helpful in music understanding, music retrieval, and other music-related applications. As volume of online musical contents expands rapidly in recent years, demands for retrieval by emotion have also been emerging. MER needs to take into the characteristics of music psychology into consideration. Although MER has been developed for years, there is currently no well-developed emotion model for music emotion representation.

In this thesis, we propose a music emotion recognition system which is based on two music formats with corresponding machine learning models. More specifically, this system includes WAVE based MER, MIDI based MER and a decision model. WAVE based MER extracts 37 features from wave files and calculates weight of each feature by RreliefF. The selected features sent to support vector machine (SVM) for training are according to sorted weights. The training data for MIDI based MER classifier, deep belief network (DBN), include time dependent and instrument features. Moreover, we also introduce the normalized algebraic product (NAP) as the decision maker for integrating the recognition from both classifiers.

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