Titlebook: Information Retrieval for Music and Motion; Meinard Müller Textbook 2007 Springer-Verlag Berlin Heidelberg 2007 Analysis.Audio Matching.Mo

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Fundamentals on Music and Audio Dataiplinary character of this research field, we start in this chapter with some fundamentals on music representations and digital signal processing. In particular, we summarize basic facts on the score, MIDI, and audio format (Sect. 2.1). We then review various forms of the Fourier transform (Sect. 2.

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Pitch- and Chroma-Based Audio Featuresne generally has to account for various aspects such as the data format (e.g., score, MIDI, audio), the instrumentation (e.g., orchestra, piano, drums, voice), and many other parameters such as articulation, dynamics, or tempo. To make music data comparable and algorithmically accessible, the first

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Audio Matching to automatically retrieve all excerpts from the database containing parts or aspects which are somehow similar to the query. This problem is particularly difficult for digital waveform-based audio data such as CD recordings. Because of the complexity of such data, the notion of . used to compare di

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SyncPlayer: An Advanced Audio Playernterrelated music data. The generated data can be used to support inter- and intradocument browsing and retrieval in complex and inhomogeneous music collections, thus allowing users to discover and explore music in an intuitive and multimodal way. To demonstrate the potentials of our MIR techniques,

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Fundamentals on Motion Capture Data virtual human characters. In this chapter, we provide the reader with some fundamental facts on motion representations. We start with a short introduction on motion capturing and introduce a mathematical model for the motion data as used throughout the subsequent chapters (Sect. 9.1).We continue wi

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DTW-Based Motion Comparison and Retrievalcontext, the choice of suitable local cost or distance measures is of crucial importance, since they determine the kind of (spatial) similarity between the elements (frames) of the two sequences to be aligned. For the mocap domain, we introduce two conceptually different local distance measures – on