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@article{192880,
        author = {Parveen Shaik and Dr. N. Veeranjaneyulu},
        title = {Sound Recognition: Identification of Single and Multiple Sounds in an Audio Clip},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2468-2474},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192880},
        abstract = {Sound recognition technology is becoming more and more crucial for comprehending and reacting to real-world acoustic environments due to the quick development of smart cities and intelligent systems. Accurately detecting single and multiple sound events in noisy and overlapping audio is still very difficult, though. The suggested work focusses on using real audio recordings to apply a Convolutional Neural Network (CNN)-based method for sound recognition in order to address this. To learn significant frequency and temporal patterns from spectrogram representations of audio data, an existing CNN architecture is used rather than creating a new model. By managing background noise and dynamic sound conditions, this method allows for the efficient classification of both isolassted and simultaneous urban sounds. In complex environments, the use of spectrogram-based features increases recognition robustness and accuracy. In summary, the suggested system offers a workable and effective real-time sound recognition solution. Applications like surveillance systems, urban safety monitoring, healthcare support, and other sound-aware intelligent technologies can all benefit from its use.},
        keywords = {Urban acoustic events, simultaneous noises, real-world sound understanding, audio detection, and sound recognition.},
        month = {February},
        }