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4 articles for “spectrograms”
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Avian Echoes: Convolutional Neural Network for Bird Vocalization Detection
Abstract: Bird species identification is a complex task within ornithology that demands advanced technological solutions. This research presents an approach leveraging Convolutional Neural Networks (CNNs) for bird species recognition based on identification of bird sound, each employing unique datasets and methodologies. The objective involves a two-stage identification process, beginning with the construction of an ideal dataset. The crucial step involves converting 1D audio waveforms to 2D spectrograms, enhancing CNNs' ability to …
Published in Journal of Aerospace Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 26–37 Read article
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Artificial intelligence and its uses in speech recognition
Abstract: Innovative technologies utilising artificial intelligence have transformed the way we live. Every sector of society has been significantly impacted by AI, which has swept through every industry. The term "artificial intelligence" was first used at a symposium in 1956. The conference's discussion on multidisciplinary information technology and natural language production. Technology advanced enormously as a result of the development of the internet. Thirty years ago, artificial intelligence technology was a …
Published in Journal of Mechatronics and Automation · Vol. 9, Issue 2, 2022 · pp. 46–49 Read article
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Acoustic Sensing for City Flow: Quasi-Supervised Recognition of Sirens and Traffic for Urban Mobility Intelligence
Abstract: This paper frames environmental audio as a mobility telemetry source, extending a benchmark urban-sound corpus with transportation-critical classes—ambulance, firetruck, police, and traffic—and training spectrogram-based models under a quasi-supervised regime to support real-time city operations; leveraging 10-fold protocols, class-weighted objectives, and audiospecific augmentations (time stretch, pitch shift, SpecAugment, PatchAugment), the system benchmarks multiple CNN backbones combined with self-supervised learning paradigms enable the extraction of rich, discriminative acoustic representations, achieving strong multi-class …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 42–50 Read article