Prem Seetharaman

Cover song identification with 2D Fourier Transform sequences

We approach cover song identification using a novel time-series representation of audio based on the 2DFT. The audio is represented as a sequence of magnitude 2D Fourier Transforms (2DFT). This representation is robust to key changes, timbral changes, and small local tempo deviations. We look at cross-similarity between these time-series, and extract a distance measure that is invariant to music structure changes. Our approach is state-of-the-art on a recent cover song dataset, and expands on previous work using the 2DFT for music representation and work on live song recognition.

SocialFX: Studying a Crowdsourced Folksonomy of Audio Effects Terms

We present the analysis of crowdsourced studies into how a population of Amazon Mechanical Turk Workers describe three commonly used audio effects: equalization, reverberation, and dynamic range compression. We find three categories of words used to describe audio: ones that are generally used across effects, ones that tend towards a single effect, and ones that are exclusive to a single effect. We present select examples from these categories. We visualize and present an analysis of the shared descriptor space between audio effects. Data on the strength of association between words and effects is made available online for a set of 4297 words drawn from 1233 unique users for three effects (equalization, reverberation, compression). This dataset is an important step towards implementing of an end-to-end language-based audio production system, in which a user describes a creative goal, as they would to a professional audio engineer, and the system picks which audio effect to apply, as well as the setting of the audio effect.

Music/Voice separation using the 2D fourier transform

Audio source separation is the act of isolating sound sources in an audio scene. One application of source separation is singing voice extraction. In this work, we present a novel approach for music/voice separation that uses the 2D Fourier Transform (2DFT). Our approach leverages how periodic patterns manifest in the 2D Fourier Transform and is connected to research in biological auditory systems as well as image processing. We find that our system is very simple to describe and implement and competitive with existing unsupervised source separation approaches that leverage similar assumptions.