# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 2 Related Work
The distance between the ranges is almost the same in the mel spectrogram. However, research work for the SED challenge in urban environments using a combination of MFCC and RASTA-PLP is limited, and requires the attention of engineers and researchers. MFCC is particularly useful for natural sounds. This feature is based on human hearing, which cannot perceive frequencies over 1 Khz. Similarly, the RASTA-PLP feature works better with artificial sounds, which have a greater likelihood of containing noise. The majority of noise-reduction methods used in voice recognition systems heavily rely on applying various filtering strategies of the RASTA-PLP filter across a range of frequency frames; for instance, a band-pass filter can be used to eliminate frames and background noise that changes slowly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature (Year)** & **SED Model** & **Classification Strategy/Network** & **Audio Environment** & **Feature(s)** \\ \hline
[1] & Baseline 2017 & Multi-layer perceptron & Urban & Log-mel energy \\ \hline
[2] & Adavanne & & & \\
2017 & TUT 1 & CRNN & Urban & Log-mel energy \\ \hline
[8] & Baseline & & & \\
2016 & 2016 & GMM & Urban & MFCC \\ \hline
[16] & Soft-Median & CB+SMC+CDPP & Domestic & Log-mel energy \\
2022 & Choice & CB+SMC+CDPP & Domestic & Log-mel energy \\ \hline
[17] & Capsule & & & \\
2022 & Network & PBA-AttCapsNet-BGRU & Smart cars & Log-mel energy \\ \hline
[13] & Audio Tagging & & & \\
2022 & Consistency Constraint & ATCC-CRNN & Domestic & Log-mel energy \\ \hline
[18] & Pipe & SVM & Pipe leakage & Time-domain features \\
2022 & Leakages & RNN & Urban & Log-mel energy \\ \hline
[20] & Vu-Task3 & RNN & Urban & Log-mel energy \\ \hline
[28] & Viterbi & & & \\
2002 & Algorithm & HMM/MLP & Clean Speech &
\begin{tabular}{c} RASTA-PLP \\ Frequency filtering \\ \end{tabular} \\ \hline
[21] & [PERSON] & & & \\
2017 & PKU 1 & LSTM & Urban & Log-mel energy \\ \hline
[22] & [PERSON] & & & \\
2017 & SNU 3 & CNN & Urban & Log-mel energy \\ \hline
[23] & [PERSON] & & & \\
2017 & THU 1 & RNN & Urban &
\begin{tabular}{c} MFCC \\ Pitch \\ \end{tabular} \\ \hline
[24] & [PERSON] & & & \\
2017 & UR 1 & CNN & Urban & Log-mel energy \\ \hline
[25] & Xia & CNN & Urban & Log-mel energy \\
2017 & UWA 3 & CNN & Urban &
\begin{tabular}{c} Scattering transfer \\ Clustering \\ \end{tabular} \\ \hline
[26] & Kroos & Neuro- & Urban &
\begin{tabular}{c} Scattering transfer \\ Clustering \\ \end{tabular} \\
2017 & CVSSP 2 & evolution & Urban & Raw audio \\ \hline
[19] & [PERSON] & & & \\
2017 & BUPT 2 & BGRU & Urban & Raw audio \\ \hline
[27] & Li Scut 2 & Bi-LSTM & Urban & MFCC \\
2017 & \
\begin{tabular}{c} Closed \\ Multi-Model \\ \end{tabular} & CRNN, 5-CRNN, and ANN & Urban &
\begin{tabular}{c} RASTA-PLP, \\ MFCC \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and comparison of related works on SED models. Therefore, there is a need to develop multi-model classification techniques based on MFCC and RASTA-PLP features for the SED challenge for both natural and artificial sounds in urban environments. The existing schemes in the literature do not address this or use both these features in their SED models. Such an approach could lead to improved overall F1-score and reduced ER. This is the main reason for our proposing a multi-model classification strategy for use in an urban environment.