Non-negative matrix factorization-based subband decomposition for acoustic source localization
This addresses the problem of improving source localization accuracy for applications like speech processing in noisy settings, but it appears incremental as it builds on existing NMF techniques.
The paper tackled acoustic source localization in noisy and reverberant environments by introducing a non-negative matrix factorization-based subband decomposition method, resulting in more accurate performance than conventional algorithms as shown in experiments with simulated and real noise.
A novel non-negative matrix factorization (NMF) based subband decomposition in frequency spatial domain for acoustic source localization using a microphone array is introduced. The proposed method decomposes source and noise subband and emphasises source dominant frequency bins for more accurate source representation. By employing NMF, delay basis vectors and their subband information in frequency spatial domain for each frame is extracted. The proposed algorithm is evaluated in both simulated noise and real noise with a speech corpus database. Experimental results clearly indicate that the algorithm performs more accurately than other conventional algorithms under both reverberant and noisy acoustic environments.