Deep and Shallow: Machine Learning in Music and Audio

結合信號和語言模型，本書探討了聲音如何被電腦系統表示和操作，以及通過信息理論的視角，我們的設備如何能夠識別特定的聲音模式並賦予其音樂上的意義或創造性。

Shlomo Dubnov is a Professor in the Music Department and Affiliate Professor in Computer Science and Engineering at the University of California, San Diego. He is best known for his research on poly-spectral analysis of musical timbre and inventing the method of Music Information Dynamics with applications in Computer Audition and Machine improvisation. His previous books on The Structure of Style: Algorithmic Approaches to Understanding Manner and Meaning and Cross-Cultural Multimedia Computing: Semantic and Aesthetic Modeling were published by Springer.

Ross Greer is a PhD Candidate in Electrical & Computer Engineering at the University of California, San Diego, where he conducts research at the intersection of artificial intelligence and human agent interaction. Beyond exploring technological approaches to musical expression, Ross creates music as a conductor and orchestrator for instrumental ensembles. Ross received his B.S. and B.A. degrees in EECS, Engineering Physics, and Music from UC Berkeley, and an M.S. in Electrical & Computer Engineering from UC San Diego.

Shlomo Dubnov是加州大學聖地牙哥分校音樂系的教授，同時也是計算機科學和工程學系的兼職教授。他以對音樂音色的多光譜分析研究和發明音樂信息動態方法在計算機聽覺和機器即興演奏方面的應用而聞名。他之前出版的書籍《風格結構：理解方式和含義的算法方法》和《跨文化多媒體計算：語義和美學建模》由Springer出版。

Ross Greer是加州大學聖地牙哥分校電機與計算機工程學系的博士候選人，他在人工智能和人類代理互動的交叉領域進行研究。除了探索技術方法來表達音樂，Ross還作為指揮和管弦樂團編曲家創作音樂。Ross在加州大學伯克利分校獲得了電機與計算機科學、工程物理學和音樂的學士學位，並在加州大學聖地牙哥分校獲得了電機與計算機工程的碩士學位。