October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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9.5
メル周波数ケプストラム係数を用いて分類器を改良する
185
9.5
メル周波数ケプストラム係数を用いて分類器を改良する
確かに、FFTを使うことは理にかなっています。しかし、それだけでは、混ぜこぜになった音楽
データをジャンルごとにフォルダ分けすることを達成できません。私たちには何らかのさらに進んだ手
法が必要です。
現時点で言えることは、さらに調査が必要であるということです。他の人がすでに同様な問題に挑
戦しているかもしれません。また、私たちの問題に役立てることのできる新しい方法を見つけることが
できる か もし れ ま せ ん。 実 際、 音 楽 ジ ャンル 分 類 の た め に、International Society for Music
Information Retrieval（ISMIR）によって、一年に一度カンファレンスが開かれています。そして、
Automatic Music Genre Classification（AMGC）という分野は音楽情報検索（Music Information
Retrieval：MIR）で確立された分野の一つです。AMGCの論文を見渡せば、自動ジャンル分類を目的と
した論文が見つかると思います。そのような論文から今回の問題に役立てるアイデアを得ることが
でき
るでしょう。
ここでは、メル周波数ケプストラム係数（Mel Frequency Cepstral Coefficient：MFCC）と呼ばれ
る適 用 範囲の 広い手 法を紹 介したいと思
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