本論文提出使用音樂旋律特徵進行自動風格分類的方法。我們比較了三種統計分類方法：以關聯度為基礎的方法、類神經網路、k個最近相似度方法。在基礎實驗中以類神經網路67.5%的正確率最高，而本論文的關聯度分類方法正確率則達到66.2%。
接著我們將旋律片段分群對關聯度為基礎的方法進行改進。在資料稀疏時，藉由群聚內的旋律片段達到機率共享的好處，可以將正確率提升到 70.0%。因此，進一步我們提出條件式平滑化方法，希望能依照訓練資料的充足與否做為判斷是否相信群聚關聯度統計值的準則。實驗結果發現，當在旋律片段出現次數排名前20%者(統計資料充足)，可以把正確率在提升到70.67%。也就是說，條件式平滑化是有助於正確率提升。

This paper proposes a scheme of using melodic patterns to classify the musical genres. Three types of statistical classification approaches are compared, correlation-based classifier, artificial neural network(ANN), K-nearest neighbor classifier. In the baseline experiment, ANN can achieve the highest accuracy of 67.5%, while the correlation-based classifier proposed in this paper the accuracy of 66.2%.
The correlation-based classifier were there improved by smoothing the statistical of correlations based on clustered melodic patterns. The accuracy of 70.0% can be achieved for correlation-based classifier after applying the smoothing approach. Finally, a scheme of conditional smoothing by considering the amount of training data can be further used to improve the accuracy up to 70.67%.

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