• Ankush Vijay Soni
• Shubham Thombare
• Kunal Sonar
• Vishal Kajale
• Pradnyesh Bhisikar

Audio analysis is useful for audio monitoring, multimedia retrieval, audio tagging, and criminal investigations. Due to the presence of multiple sound sources, background noises, and the presence of overlapping or polyphonic contexts, environmental audio scene recognition (EASR) and sound event recognition (SER) for audio monitoring are difficult tasks. We concentrate on developing strong and efficient descriptions for essential aspects of ambient audio scenarios, which have shown to be successful in language and audio-related activities For the SER task, the Environmental Sound Classification (ESC-50) dataset is used. The exclusionary character of model-driven presentations improves EASR and SER setting a good example. The existing solutions are better suited to duties with small datasets.

EASR (Environmental Audio Scene Recognition), SER (Sound Event Recognition), STFT (Short-Time Fourier Transform), FFT (Fast Fourier Transform), Frequency, Amplitude, Timbre(Pitch)

"SURROUNDING AUDIO SCENE AND EVENT IDENTIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.d104-d107, July-2021, Available :http://www.jetir.org/papers/JETIR2107404.pdf

"SURROUNDING AUDIO SCENE AND EVENT IDENTIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppd104-d107, July-2021, Available at : http://www.jetir.org/papers/JETIR2107404.pdf