Sub-Nyquist Audio Fingerprinting for Music Recognition

Abstract—In recent years, Compressive Sampling (CS), a new research topic in signal processing, has piqued interest of a wide range of researchers in different fields. In this paper, we present a sub-Nyquist Audio Fingerprinting (AF) system for music recognition, which utilizes CS theory to generate a compact audio fingerpint, and to achieve significant reduction of the dimensionality of the input signal. The presented experimental results demonstrate that by using the CS-based sub-Nyquist AF system, when downsampling to 30%, the average accuracy is 93.43 % under various distorted environments, compared to Nyquist sampling methods. The advantages of the proposed process lie in the comparable performance under the sub-Nyquist sampling rate, and more compact audio fingerprint. I

Coactive neuro-fuzzy modelling for colour recipe prediction

1. Introduction / 2. CANFIS Concept / 3. CANFIS modeling for Color Recipe prediction / 4. S imulation / 5. Discussion / 6. ConclusionExplores neuro-fuzzy approaches to computerized colour recipe prediction which relates surface spectral reflectance of a target colour to several colorant proportions. The approaches are expressed within the framework of CANFIS (co-active neuro-fuzzy inference system) where both neural networks (NNs) and fuzzy systems (FSs) play active roles together in pursuit of a given task. To find an ideal adaptive model for this problem, the authors have investigated a variety of structures, they feature knowledge-embedded architectures and an adaptive FS, which serves to determine colour selection. They have enormous potential for augmenting prediction capability