Abstract
Most existing automatic chord recognition systems use a chromagram in front-end processing and some sort of classifier (e.g., hidden Markov model, Gaussian mixture model (GMM), support vector machine, or other template matching technique). The vast majority of front-end algorithms derive acoustic features based on a standard short-time Fourier analysis and on mapping energy from the power spectrum, or from a constant-Q spectrum, to chroma bins. However, the accuracy of the resulting spectral representation is a crucial issue. In fact, conventional methods based on short-time Fourier analysis involve an intrinsic trade-off between time resolution and frequency resolution. This work investigates an alternative feature set based on time-frequency reassignment, which was applied in the past to speech processing tasks such as formant extraction. As shown in the following experiments, the reassigned spectrum provides a very accurate front-end for the GMM-based chord recognition system here investigated.
Highlights
With the rapid growth of digital media and musical collections that can be accessed via the Web, many new applications are presently envisaged which require the analysis of audio contents
Automatic chord recognition has always been of great interest, since a chord sequence can act as a robust mid-level representation for a variety of music information retrieval (MIR) tasks such as cover song identification, music classification, and retrieval [1]
5 Experimental results we describe a series of experiments that show the convenience of using Harmonic reassigned chroma (HRC) features in the front-end processing
Summary
With the rapid growth of digital media and musical collections that can be accessed via the Web, many new applications are presently envisaged which require the analysis of audio contents. Studies on feature extraction for chord recognition started with the introduction of chroma features, to the spectrogram for speech analysis, the chromagram (i.e., a sequence of chroma vectors) is one of the most effective signal representations for music analysis and chord recognition. Several works proposed the use of some sort of harmonic analysis in order to reveal the presence of higher harmonic components [10,11,12]. In all these approaches, spectral analysis is performed on a frame-by-frame basis, in order to find all the pitches that occur at each time instant
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
