Download High frequency magnitude spectrogram reconstruction for music mixtures using convolutional autoencoders We present a new approach for audio bandwidth extension for music signals using convolutional neural networks (CNNs). Inspired by the concept of inpainting from the field of image processing, we seek to reconstruct the high-frequency region (i.e., above a cutoff frequency) of a time-frequency representation given the observation of a band-limited version. We then invert this reconstructed time-frequency representation using the phase information from the band-limited input to provide an enhanced musical output. We contrast the performance of two musically adapted CNN architectures which are trained separately using the STFT and the invertible CQT. Through our evaluation, we demonstrate that the CQT, with its logarithmic frequency spacing, provides better reconstruction performance as measured by the signal to distortion ratio.
Download Tiv.lib: An Open-Source Library for the Tonal Description of Musical Audio In this paper, we present TIV.lib, an open-source library for the
content-based tonal description of musical audio signals. Its main
novelty relies on the perceptually-inspired Tonal Interval Vector
space based on the Discrete Fourier transform, from which multiple instantaneous and global representations, descriptors and metrics are computed—e.g., harmonic change, dissonance, diatonicity, and musical key. The library is cross-platform, implemented
in Python and the graphical programming language Pure Data, and
can be used in both online and offline scenarios. Of note is its
potential for enhanced Music Information Retrieval, where tonal
descriptors sit at the core of numerous methods and applications.