# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 2 Related Work
They demonstrated that Binaural MFCC shows superior results to the monochannel approach by comparing Monaural mel-frequency cepstral coefficients MFCC with Binaural MFCC. In [21], the authors combined an LSTM network with log-mel-band energy characteristics. To extend their model, they built three separate channels and employed several data fusion techniques. Experimental results demonstrated that their methodology outperformed the baseline in terms of performance. The authors in [22] introduced a CNN model using both short-term and long-term data as input. Additionally, they described a number of optimization techniques, including class-wise early stopping and frequent validation with adaptive thresholds. Comparing the suggested framework to the baseline system, there were noticeable improvements. To simulate the temporal evolution of sound occurrences, the ref. [23] first introduced a multi label bi-RNN. Additionally, the authors suggested using data augmentation to address the issue of data scarcity and investigated the most effective augmentation techniques to improve performance. The two channels were combined into one in [24], and the converted spectrum was calculated using the mel-band energy. In this paper, training was carried out utilizing a CNN, showing the feasibility of doing so for SED. The work in ref. [25] provided a CNN-based SED using a class-wise distance-based approach. By calculating the difference between the audio features of each frame and the class-wise distance of each event, the CNN output could be modified. The detected sound segments were then segmentally re-evaluated using the class-wise distances. The authors in ref. [26] proposed a more developed version of Neuro-Evolution of Augmenting Topologies (NEAT). They investigated the use of small networks that might compete the much larger networks currently used for SED. In their work, use of k-means clustering and wavelet-based deep scattering transform were employed with NEAT for a more compact representation of the sound input. The multi-model system put forth in [19] uses DNN to identify car-related sound events. Five models were built on CRNN to identify other sound events, such as children, large vehicles, people speaking, and people walking. Raw audio and log-mel energies were used as input to the features. In [20], a 20 ms window size was used with a Hamming window and a 10 ms hop-length, and inputs were transformed into a time-frequency representation. MFCC was used as the input feature. The authors divided each audio characteristic into a one-second window, which they then broadcast into a CRNN network. The two primary components of their framework [27] were feature extraction and Bi-LSTM classification. In the light of above discussion, the existing works in the literature are summarized and compared in Table 1 based on their different SED models and different classification strategies (networks) in different environments with various sound features. In this review, it can be seen that most of the previous works are multi-feature, e.g., existing research works have considered either MFCC, log-mel energy, pitch, or a combination of these. For example, researchers have employed time-domain features, including parameters such as short time energy, zero crossing rate, gradient index, etc., to represent a signal in the time domain, e.g., signal amplitude values. The pitch feature represents the perception of a sound frequency, e.g., low pitch sounds are associated with low frequency sound waves, and high pitch sounds are associated with high frequency sound waves. Similarly, a sound is processed in the frequency domain via frequency filters. It is first converted using Fourier analysis, then multiplied using the filter function, and then transformed back into the spatial domain. The log-mel energy represents frequencies logarithmically (thecorner frequency) over a specific threshold. For instance, in a spectrogram with a linear scale, the vertical space between 1000 and 2000 Hz is half that between 2000 and 4000 Hz.