Real-time scheduling and computer accompaniment
Current directions in computer music research
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Adaptive mixtures of local experts
Neural Computation
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A modular neuro-fuzzy network is proposed for the classification of musical instruments from the sound they produce. Each module, which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data, operates on a distinct subset of input features. All sub-networks are separately initialized and trained by a two-phase strategy. First, a fuzzy clustering algorithm is applied to establish the structure of each sub-network as well as the initial values of its parameters. Then, each sub-network enters a supervised learning phase for optimal adjustment of its parameters. After learning, each sub-network encodes in its structure the knowledge learned in the form of fuzzy if-then rules. The various sub-networks are then combined in a single modular network that is able to face the complete classification task. Preliminary experimental results compare favorably with human performance on the same task and demonstrate the utility of the modular approach.