Nonnegative Matrix Factorization is a popular tool for the analysis of audio spectrograms. It is usually initialized with random data, after which it iteratively converges to a local optimum. In this paper we show that N-FINDR and NNLS, popular techniques for dictionary and activation matrix learning in remote sensing, prove useful to create a better starting point for NMF. This reduces the number of iterations necessary to come to a decomposition of similar quality. Adapting algorithms from the hyperspectral image unmixing and remote sensing communities, provides an interesting direction for future research in audio spectrogram factorization.

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