Dictionary extraction from a collection of spectrograms for bioacoustics monitoring

Dictionary learning of spectrograms consists of detecting their fundamental spectra-temporal patterns and their associated activation signals. In this paper, we propose an efficient convolutive dictionary learning approach for analyzing repetitive bioacoustics patterns from a collection of audio recordings. Our method is inspired by the convolutive non-negative matrix factorization (CNMF) model. The proposed approach relies on random projection for reduced computational complexity. As a consequence, the non-negativity requirement on the dictionary words is relaxed. Moreover, the proposed approach is well-suited for a collection of discontinuous spectrograms. We evaluate our approach on synthetic examples and on two real datasets consisting of multiple birds audio recordings. Bird syllable dictionary learning from a real-world dataset is demonstrated. Additionally, we apply the approach for spectrogram denoising in the presence of rain noise artifacts.