International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
www.irjet.net
ONE SCREAM-Human Scream Detection and Analysis Sharvani Banala, Sai Niveditha Bukka, Manikonda Vaishnavi Sharvani Banala, B Tech -CSE, Malla Reddy University Sai Niveditha Bukka, Tech -CSE, Malla Reddy University Manikonda Vaishnavi, B Tech -CSE, Malla Reddy University Guide: Mrs. Shailaja Musham, Assistant Professor, School of Engineering, Malla Reddy University ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Over the last decade, extensive research indicates
including healthcare, security, and entertainment. This research aims to delve into the intricate details of human scream detection and analysis, uncovering underlying patterns, physiological mechanisms, and potential applications of this primal vocalization. Scream detection is pivotal in fields such as safety, security, and healthcare, aiding in identifying and responding to distress signals effectively. The Yin algorithm, a pitch detection algorithm widely used in speech and audio processing, presents an effective method for detecting screams. Grounded in autocorrelation, the Yin algorithm accurately estimates pitch even amidst noise, making it suitable for detecting high-pitched and intense sounds characteristic of screams. By analyzing pitch variations in real-time audio data, the Yin algorithm distinguishes scream-like patterns from ambient noises, facilitating prompt and reliable scream detection. Integrating the Yin algorithm into scream detection systems enhances capabilities, enabling swift response in emergencies and improving public safety. Applications range from smart home security to public surveillance and healthcare monitoring. The problem statement lies in the need to develop a robust scream detection system capable of operating reliably in diverse acoustic environments, filtering out extraneous noise while accurately recognizing high-intensity vocalizations. This challenge requires advanced signal processing algorithms, like the Yin algorithm, to tackle real-world scenarios. The system must be effective in distinguishing scream patterns and overcoming obstacles related to realtime implementation, adaptability, and integration into existing safety infrastructure. Addressing these challenges can improve emergency response mechanisms, elevate public safety standards, and contribute to various applications. Successfully resolving this challenge holds the potential to enhance overall response efficiency and contribute to the development of more reliable and adaptable scream detection systems.
a notable surge in global crime rates, with women bearing a disproportionate impact. Safeguarding women has become a critical concern in response to this escalating trend. Recognizing the imperative for innovative solutions to tackle the rising crime rates, this study introduces a distinctive project centered on Human Scream Detection and Analysis, employing cutting-edge machine learning and deep learning techniques. The research seeks to explore the identification of human screams through acoustic analysis, leveraging machine learning to differentiate screams from background noise. The proposed system holds promise for applications in various domains, including public safety, emergency response, and healthcare. The methodology involves feature extraction and classification to heighten the accuracy of scream detection, contributing to improved real-time recognition and response mechanisms. This study provides a valuable contribution to the evolving fields of audio analysis and machine learning, offering a comprehensive approach to human scream detection. The potential applications in emergency response, public safety, and mental health underscore its significance across diverse domains. The findings underscore the importance of aligning technological advancements with ethical considerations to ensure responsible and beneficial deployment in real-world scenarios. Furthermore, the research delves into the ethical considerations associated with deploying such technology. Privacy concerns, potential misuse, and the psychological impact on monitored individuals are meticulously examined. The study proposes recommendations for the responsible implementation and continual refinement of the technology to address these ethical considerations.
Key Words: Human Scream Detection, Acoustic Analysis, Public Safety, Feature Extraction, Ethical Considerations, Potential Misuse, Responsible Implementation, Audio analysis
1.INTRODUCTION
1.1 Problem Statement
Exploring human vocalizations has long fascinated researchers across disciplines, encompassing a wide array of emotions, communicative signals, and physiological responses. Among these vocal expressions, the scream stands out as a primal and potent indicator of intense emotions, with applications spanning psychology, neuroscience, and technology. Understanding and analyzing human screams carry significant implications across diverse domains,
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In real-world scenarios, the quick and accurate identification of screams is crucial for maintaining safety and security. Current audio processing systems often face difficulties in differentiating genuine distress sounds, like screams, from background noise, which can result in delayed or incorrect responses. Therefore, there is a pressing need for a highly effective scream detection system that can function
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