Voice-based Mobile Phone Brand Identification

Brand identification is important for security, marketing, and user experience as the use of mobile devices increases. This study uses Mel Frequency Cepstral Coefficients (MFCC) and the Random Forest algorithm to develop an innovative approach for voice-based brand identification for mobile devices. The process of identifying and confirming the legitimacy of a brand using audio data, such as brand-specific music, slogans, or conversations, is known as voice-based brand identification. Due to its ability to offer safe access to branded services and improve user experience, it is of major importance to both businesses and consumers. In this study, a comprehensive framework for voice-based brand identification is proposed. The foundation of our methodology is the extraction and examination of MFCC features. The distinctive spectrum characteristics of audio signals are captured by MFCC features, which makes them useful for brand-specific identification. The Random Forest method is used for categorization, offering an authentic and precise way to differentiate between various brands. Using the MFCC feature set as input, the Random Forest model is trained and refined for identifying brands. The outcomes show that the system is capable of accurately and effectively identifying brands. Performance measurements included accuracy, precision, recall and F1 score. This study contributes to the rapidly developing field of audio-based brand recognition and creates emerging possibilities for improving mobile device security and user experiences.