![]() ![]() The last decade has seen a paradigm shift from expert-designed algorithms to data-driven approaches. The applications of audio and music processing range from music discovery and recommendation systems over speech enhancement, audio event detection, and music transcription, to creative application s such as sound synthesis and morphing. The algorithm is ported and run in a Linux based set top box connected by a microphone array to capture the audio for live scream detection. Experiments of screaming detection is discussed with promising results shown. Finally, to validate the scream like sound, a SVM based classifier is applied with the feature vector generated from the MFCCs across a window of frames. Further, a robust high pitch detection based on the autocorrelation is presented to extract the highest pitch for each frame, followed by a pitch analysis for a time window containing multiple frames. We adopt the log energy to detect the energy continuity of the audio to represent the screaming which is often lasting longer than many other sounds. In audio features, sound energy is a useful feature to detect scream like audio. We present here an approach to scream detection, using both analytic and statistical features for the classification. For home care, elder care, and security application, screaming is one of the events people (family member, care giver, and security guard) are especially interested in. It provides complementary information for video signal. A proper resonance strategy in reinforced falsetto and a decreased glottal adduction in growl voice might probably be the factors that contribute to prevent voice problems in singers who use these vocal resources, classically labeled as vocal abuse.Īudio signal is an important clue for the situation awareness. Reinforced falsetto is characterized by an increased vocal fold adduction and an increased Psub. It seems that growl voice is produced by decreasing vocal folds adduction and increasing Psub, which in turn, promotes an increased airflow rate. No differences for F0 were found for neither growl voice nor reinforced falsetto. Higher Psub, sound pressure level, and glottal resistance for high-pitched reinforced falsetto compared to naïve falsetto (keeping the same F0) were found. Higher glottal airflow rate, sound pressure level, and subglottic pressure (Psub) for growl voice samples compared to vowel production without growl voice (keeping the same fundamental frequency ) were found. Then, subjects were aerodynamically assessed while performing growl voice or reinforced falsetto. All participants were asked to undergo rigid laryngeal videostroboscopy to confirm the absence of laryngeal pathology. Sixteen participants performed growl voice and seven performed reinforced falsetto as a voice resource during metal singing. The present study aimed to assess the aerodynamic characteristics of vocally healthy metal singers when producing growl voice or reinforced falsetto.įifty-four participants (metal singers) were initially enrolled in this study, with 23 meeting the inclusion criteria. ![]()
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