An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applications (Hardcover)

An Introduction to Audio Content Analysis

Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches

An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation.

To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website.

Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include:

• Digital audio signals and their representation, common time-frequency transforms, audio features
• Pitch and fundamental frequency detection, key and chord
• Representation of dynamics in music and intensity-related features
• Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment
• Audio fingerprinting, musical genre, mood, and instrument classification

An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.

《音頻內容分析入門》

《音頻內容分析入門》是一本全面介紹音頻內容分析的指南，解釋了如何利用信號處理和機器學習方法從音頻中提取音樂內容。它使讀者能夠理解算法，教導計算機解讀音樂信號，並設計與音樂互動的工具。本書將音頻信號處理和機器學習的主題結合在一起，展示如何使用音頻內容分析自動捕捉音樂特徵。書中介紹了與音樂信號的音調、時間、音色和強度相關的多種音頻內容分析任務。每個任務都從音樂和技術的角度進行介紹，詳細介紹算法方法，並提供實施細節和評估的實用指導。

為了幫助讀者理解，每個任務描述都以對所涵蓋主題的最重要的音樂和感知特徵的簡短介紹開始，然後是詳細的算法模型和評估，最後是問題和練習。對於感興趣的讀者，可以通過附帶的網站獲取更新的補充材料。

本書由音樂行業的知名專家撰寫，其中包括以下主題：

- 數字音頻信號及其表示，常見的時頻轉換，音頻特徵
- 音高和基頻檢測，音調和和弦
- 音樂中動態的表示和與強度相關的特徵
- 節拍直方圖，起始點和節奏檢測，音樂結構的檢測和序列對齊
- 音頻指紋，音樂類型，情緒和樂器分類

《音頻內容分析入門》對於音頻信號處理的新手和行業專家都是一本寶貴的指南，涵蓋了與音樂信息檢索和機器聆聽相關的廣泛入門主題，使學生和研究人員能夠快速獲得音頻分析的核心整體知識，並通過大量參考資料深入研究該領域的特定方面。

Alexander Lerch, PhD, is an Associate Professor at the Center for Music Technology, Georgia Institute of Technology. His research focuses on signal processing and machine learning applied to music, an interdisciplinary field commonly referred to as music information retrieval. He has authored more than 50 peer-reviewed publications and his website, www.AudioContentAnalysis.org, is a popular resource on Audio Content Analysis, providing video lectures, code examples, and other materials.

Alexander Lerch, PhD 是喬治亞理工學院音樂科技中心的副教授。他的研究專注於應用於音樂的信號處理和機器學習，這是一個跨學科領域，通常被稱為音樂信息檢索。他已經發表了50多篇同行評審的論文，他的網站www.AudioContentAnalysis.org是一個在音頻內容分析方面的熱門資源，提供視頻講座、代碼示例和其他材料。