Cluster Analysis for the Separation of Auditory Scenes

Abstract

The "cocktail party effect" refers to the ability of human listeners to separate the acoustic signal reaching their ears into its individual components, corresponding to individual sound sources in the environment. Despite this phenomenon appearing trivial for humans, implementing the cocktail party effect computationally remains an ambitious challenge. The approach used in this paper takes inspiration from human strategies for separating an acoustic environment into distinct perceptual auditory streams. A series of time-frequency-based features, analogous to those thought to emerge at various stages in the human auditory processing pathway, are derived from biaural auditory inputs. These feature vectors are used as inputs to an unsupervised cluster analysis used to group feature values that are assumed to correspond to the same object. Reconstructed auditory streams are then correlated to the original components used to create the auditory scene. Our model is capable of reconstructing streams that correlate to the original components (r = 0.3-0.7) used to create the complex auditory scene. The success of the reconstructions is largely dependent on the signal-to-noise ratio of the components of the auditory scene.

Short Term Power Load Combination Forecasting Method Based on Feature Extraction

“…Enhancing model accuracy is particularly crucial accordingly. Due to the time series and nonlinear characteristics of load sequences, numerous scholars have proposed a variety of models by simple or complex methods such as machine learning [4], exponential smoothing method [5], autoregressive integrated moving average model [6], multiple linear regression method [7], Kalman filter algorithm [8], grey prediction theory [9] and support vector machine [10]. Nevertheless, their overall forecasting accuracy has room for improvement, especially by the, challenge of applying large-scale data with universality.…”