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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178
9
章 クラス分類
III
：音楽ジャンル分類
実際の音楽データでは、フーリエ変換を行うと先ほどの例のようなシンプルな形状になることはあり
ません。たとえば、図9-3のようになります。
図9-3
9.4
FFT
を用いて最初の分類器を作成する
とりあえず、FFTを用いて音楽を識別できる“指紋”なるものを作成したいと思います。いくつかの
音楽を対象にFFTを行い、対応するジャンルをラベルとして設定したら、訓練データは準備できたこ
とになります。そうすれば、後は分類器に入力するだけです。
9.4.1
機敏に実験する
分類器の訓練を行う前に、いろんな実験を素早く行えるように工夫することから始めたいと思いま
す。FFT（高速フーリエ変換）は“高速”に行うことができますが、それでもテキストベースの章で特
徴量を生成した時と比べると時間がかかります。まだ私たちは実験フェーズであり、特徴量を生成す
るプロセスをできるだけ高速にしたいと考えるのはもっともなことでしょう。
もちろん、各ファイルに対してFFTを行った結果は毎回同じです。そのため、あらかじめFFTを行
い、その結果をキャッシュとして保存し、使用するときにそ
のキャッシュデータを読み込むようにしま
す。これを行うための関数を
create_fft()
とします。
create_fft()
の内部では、順番にFFTを
行うために、
scipy.fft()
を用います。便宜上（そして、高速化のため）、この例ではFFTが返す配
音楽データのFFT
音楽データ
時間［S］
周波数［Hz］
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.