Salvato in:
| Autore principale: | |
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| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
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| Accesso online: | https://doi.org/10.5281/zenodo.17292447 |
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Sommario:
- <div> <div>This thesis presents the design, implementation, and evaluation of GRASS, a near real-time MLOps pipeline for crowd sentiment detection from audio. The system addresses the challenge of inferring collective audience sentiment from ambient audio signals, a task relevant to atmosphere monitoring and crowd management in football stadiums.</div> <br> <div>Developed within a Design Science Research (DSR) framework, the pipeline integrates a C++/FFTW backend for sliding-window segmentation, spectrogram generation, and a ResNet-18 classifier, orchestrated through an MLOps stack of Dagster, MLflow, MinIO, InfluxDB, and Grafana, with human-in-the-loop retraining supported via Label Studio and FastAPI. On the Emotional Crowd Sound Dataset (ECSD), GRASS achieved an F1-score of 96%. In addition, a real-time streaming experiment using a laptop microphone demonstrated average per-segment inference latency of 143.6 ms and an end-to-end response time of 336.9 ms per 2 s audio clip, confirming that the pipeline processes input well within near real-time requirements.</div> <br> <div>The scope of validation remains limited. ECSD is proxy data that does not capture the acoustic and cultural complexity of football crowds, and the streaming experiment was restricted to a single-microphone local setup, not a full stadium deployment. As a result, the accuracy of sentiment detection in authentic football stadium environments remains unresolved.</div> <br> <div>The contribution of this thesis is therefore architectural and methodological: the realization of a reproducible, extensible pipeline and its validation as a proof-of-concept on available data. Future work must focus on testing with authentic stadium recordings, scaling to multi-microphone deployments, and embedding ethical safeguards to ensure responsible deployment.</div> </div>