# A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification

## TL;DR

This paper introduces a hybrid acoustic scene classification system combining multi-channel i-vectors and CNNs, achieving top performance in the DCASE-2016 challenge by leveraging their complementary strengths.

## Contribution

It proposes a novel multi-channel i-vector extraction method, a CNN architecture for ASC, and a hybrid score fusion approach that significantly improves classification accuracy.

## Key findings

- Hybrid system ranked 1st in DCASE-2016 challenge.
- Multi-channel i-vectors improve indoor and outdoor scene recognition.
- CNNs capture complementary features to i-vectors, enhancing performance.

## Abstract

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art.