# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
###### Abstract  
The Sound Event Detection task aims to determine the temporal locations of acoustic events in audio clips. In recent years, the relevance of this field is rising due to the introduction of datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). In this paper, we analyze the performance of Sound Event Detection systems under diverse artificial acoustic conditions such as high- or low-pass filtering and clipping or dynamic range compression, as well as under an scenario of high overlap between events. For this purpose, the audio was obtained from the Evaluation subset of the DESED dataset, whereas the systems were trained in the context of the DCASE Challenge 2020 Task 4. Our systems are based upon the challenge baseline, which consists of a Convolutional-Recurrent Neural Network trained using the Mean Teacher method, and they employ a multiresolution approach which is able to improve the Sound Event Detection performance through the use of several resolutions during the extraction of Mel-spectrogram features. We provide insights on the benefits of this multiresolution approach in different acoustic settings, and compare the performance of the single-resolution systems in the aforementioned scenarios when using different resolutions. Furthermore, we complement the analysis of the performance in the high-overlap scenario by assessing the degree of overlap of each event category in sound event detection datasets. s +
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categories belonging to the domain of domestic environments and also uses AudioSet as a source for audio recordings, in addition to Vimeo ([[http://vimeo.com](http://vimeo.com)]([http://vimeo.com](http://vimeo.com)) (accessed on 6 December 2021)) and synthetic recordings that are generated by overlapping target events and recordings of background sounds. Moreover, the competitive evaluations organized by the DCASE (Detection and Classification of Acoustic Scenes and Events) community [5] greatly supported the recent research in the field, and helped to define benchmarks not only for Sound Event Detection, but also for other related tasks such as Acoustic Scene Classification [6] or Anomalous Sound Detection [7]. The DCASE Challenge proposes the task \"Sound event detection and separation in domestic environments\", with the goal of determining the temporal boundaries of sound events in 10-second audio clips and classifying them. In the last years, different deep-learning-based approaches were proposed for the task of Sound Event Detection [8]. Most of them rely on the use of convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both (convolutional neural networks, or CRNN) [9; 10; 11; 12; 13; 14; 15]. These neural networks are usually trained over audio features such as the Short-Time Fourier Transform (STFT), mel-spectrograms, or mel-frequency cepstral coefficients (MFCC) [16], in which a bank of filters is applied to short segments of the input audio signal, obtaining a time-frequency representation of the audio. Some recent approaches have also employed attention-based networks [17; 18; 19] like Conformers [20], which were originally proposed for Speech Recognition. Whereas the aforementioned deep-learning methods are widely used for other signal processing tasks (e.g., CNN and CRNN for image representation [21; 22] or music tagging [23], and conformers for speaker diarization [24] or audio-visual speech recognition [25]), other works tried to further improve SED performance by tackling specific problems like domain mismatch between synthetic and real data [26; 27], or by training a sound separation front-end [28], which aims to aid the detection system.