Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models

TL;DR

This paper introduces a novel Hard Label Criteria (HLC) algorithm for assessing acoustic non-stationarity, enabling efficient supervised learning models like NANSA to accurately evaluate stationarity in real-time.

Contribution

The paper proposes the HLC algorithm for global non-stationarity labeling and develops NANSA, the first network leveraging HLC for acoustic non-stationarity assessment with high accuracy.

Findings

HLC effectively captures stationarity information in acoustic models.

NANSA achieves up to 99% classification accuracy.

The approach reduces computational costs of traditional measures.

Abstract

Objective non-stationarity measures are resource intensive and impose critical limitations for real-time processing solutions. In this paper, a novel Hard Label Criteria (HLC) algorithm is proposed to generate a global non-stationarity label for acoustic signals, enabling supervised learning strategies to be trained as stationarity estimators. The HLC is first evaluated on state-of-the-art general-purpose acoustic models, demonstrating that these models capture stationarity information. Furthermore, the first-of-its-kind HLC-based Network for Acoustic Non-Stationarity Assessment (NANSA) is proposed. NANSA models outperform competing approaches, achieving up to 99% classification accuracy, while solving the computational infeasibility of traditional objective measures.