Audio Content Analysis
Preprint for a book chapter introducing Audio Content Analysis. With a focus on Music Information Retrieval systems, this chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches to audio content analysis. The various tasks in Audio Content Analysis are categorized into three classes: music transcription, music performance analysis, and music identification and categorization. The examples for music transcription systems include music key detection, fundamental frequency detection, and music structure detection. Music performance analysis systems feature an overview of beat and tempo detection approaches as well as music performance assessment. The covered music classification systems are audio fingerprinting, music genre classification, and music emotion recognition. The chapter concludes with a discussion and current challenges in the field and a speculation on future perspectives.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionGeneral ClassificationGenre classificationInformation RetrievalKey DetectionMusic ClassificationMusic Emotion RecognitionMusic Genre ClassificationMusic Information RetrievalMusic TranscriptionRetrievalSimilar Papers 제목 키워드 기반
Automatic Organisation and Quality Analysis of User-Generated Content with Audio Fingerprinting
The increase of the quantity of user-generated content experienced in social media has boosted the importance of analysing and organising the content by its quality. Here, we propose a method that uses audio fingerprinti…
Weakly Supervised Scalable Audio Content Analysis
Audio Event Detection is an important task for content analysis of multimedia data. Most of the current works on detection of audio events is driven through supervised learning approaches. We propose a weakly supervised …
Event DetectionMultiple Instance LearningWeakly-supervised LearningAudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Recently, sound recognition has been used to identify sounds, such as car and river. However, sounds have nuances that may be better described by adjective-noun pairs such as slow car, and verb-noun pairs such as flying …
TAGAn Empirical Study of Speech Language Models for Prompt-Conditioned Speech Synthesis
Speech language models (LMs) are promising for high-quality speech synthesis through in-context learning. A typical speech LM takes discrete semantic units as content and a short utterance as prompt, and synthesizes spee…
In-Context LearningSpeech SynthesisSafeEar: Content Privacy-Preserving Audio Deepfake Detection
Text-to-Speech (TTS) and Voice Conversion (VC) models have exhibited remarkable performance in generating realistic and natural audio. However, their dark side, audio deepfake poses a significant threat to both society a…
Audio Deepfake DetectionDeepFake DetectionFace SwappingPrivacy Preserving+3