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Guitar audio signal classification has its main application in chord transcription and guitar tutoring systems. This paper proposes a method to classify chords from an electric guitar. The method performs Wavelet Decomposition in order to split the signal in approximation coefficients and details, and then, those approximation coefficients below a threshold are removed. This filtered signal is reconstructed...
This paper presents a feedback framework that can improve chord recognition for music audio signals by performing approximate note transcription with Bayesian non-negative matrix factorization (NMF) using prior knowledge on chords. Although the names and note compositions of chords are intrinsically linked with each other (e.g., C major chords are highly likely to include C, E, and G notes, and those...
For a given piece of music, there often exist multiple versions belonging to the symbolic (e.g., MIDI representations), acoustic (audio recordings), or visual (sheet music) domain. Each type of information allows for applying specialized, domain-specific approaches to music analysis tasks. In this paper, we formulate the idea of a cross-version analysis for comparing and/or combining analysis results...
This paper presents an efficient method for chord transcription of music signals. A succession of chroma vectors is calculated from the signal in order to extract the musical content of the piece over time. We introduce a set of chord templates for several types of chords (major, minor, dominant seventh, ...): different chord models taking into account one or more harmonics of the notes of the chord...
The chord progression of a song is an important high-level feature which enables indexing as well as deeper analysis of musical recordings. Different approaches to chord recognition have been suggested in the past. Though their performance increased, still significant error rates seem to be un-avoidable. One way to improve accuracy is to try to correct possible misclassifications. In this paper, we...
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