A Methodology for Real-Time Audio Event Detection and Generation Using Spectral Flux Analysis

The manual annotation of audio events is a time-intensive and complex bottleneck that severely limits the scalable creation of synchronized content and restricts user personalization in applications. This paper details an automated methodology to address this problem by generating a structured event map from any given MP3 file. The system employs a real-time audio analysis process using the Fast Fourier Transform (FFT) and calculates Spectral Flux to identify significant transient events. A dynamic thresholding technique distinguishes these events, while Beats Per Minute (BPM) analysis refines the output’s temporal accuracy. Experimental evaluation revealed a crucial discrepancy: while the system achieves high temporal precision with a Mean Absolute Error (MAE) ranging from 0.0631 to 0.0991 s, its overall BPM detection accuracy was only 50%. This technical gap was reflected in user testing, where the core event generation output received a moderate score of 3.19 out of 5, primarily due to a high false-positive rate in complex audio. The study concludes that while the methodology is effective for temporal alignment, future work must focus on more advanced adaptive thresholding to improve event selectivity and bridge the gap between technical accuracy and perceived quality.