隨著科技的進步，網際網路已充斥在我們的生活之中。音樂也不再以硬體儲存的方式流傳（例如ＣＤ、黑膠唱片），而是轉變為數位音樂的方式，透由網路平台散播。許多數位音樂串流服務平台網站也如雨後春筍般誕生，例如iTunes、Spotify、Musicovery。加上文化水平的提升，音樂已是現代人生活之中，不可或缺的一部分。世界上的音樂難以計數，如何將音樂分門別類做好管理乃為現代商業應用的一個重要課題。因此，音樂曲風自動化辨識的技術確實為一個實用且難以迴避的課題。

過去在曲風自動化辨識已有許多研究，但內容不外乎音訊處理、頻譜轉換、特徵擷取、特徵降維、監督式學習機。在相同的模式下提出各種改良，或是全新的特徵擷取…諸如此類，而辨識率也達到了七成以上。本篇論文採用不同於以往的做法，將訊號進行頻譜轉換後層層降維，所得之訊號搭配LDA與決策樹進行辨識，最後去比較與分析離散餘弦轉換與小波轉換在辨識率上的優劣。我們發現搭配小波轉換與混合LDA及決策樹的方法，可以將音樂曲風之分辨率達到八成五以上。

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