Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Multi-Class Audio Classification in Noisy In-Vehicle
  Environment   With ever-increasing number of car-mounted electric devices and their
complexity, audio classification is increasingly important for the automotive
industry as a fundamental tool for human-device interactions. Existing
approaches for audio classification, however, fall short as the unique and
dynamic audio characteristics of in-vehicle environments are not appropriately
taken into account. In this paper, we develop an audio classification system
that classifies an audio stream into music, speech, speech+music, and noise,
adaptably depending on driving environments including highway, local road,
crowded city, and stopped vehicle. More than 420 minutes of audio data
including various genres of music, speech, speech+music, and noise are
collected from diverse driving environments. The results demonstrate that the
proposed approach improves the average classification accuracy up to 166%, and
64% for speech, and speech+music, respectively, compared with a non-adaptive
approach in our experimental settings.

11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin