Exploring Hybrid Techniques for Enhanced Pitch Estimation in Speech Processing

Abstract

1. To develop a hybrid approach combining the Pitch Estimation Filter (PEF) and Cepstrum Pitch Determination (CPD) methods for pitch detection in audio signals. 2. To conduct comparative analysis with existing pitch detection methodologies, including Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics (SRH) and Cepstrum Pitch Determination (CEP), to assess the performance and accuracy of the proposed hybrid approach. 3. To evaluate the effectiveness of the hybrid approach in various real-world applications such as speech recognition and music transcription, using performance metrics including Gross Pitch Error (GPE) and classification accuracy through a K-Nearest Neighbors (KNN) classifier. The study discussed the difficulties in assessing pitch detection algorithms in real-world applications, especially when it comes to audio synthesis and music production. Prominent performance metrics and criteria pertinent to pitch tracking in interactive music applications were identified by the authors through comprehensive user studies and surveys with audio engineers and professional musicians. The results demonstrated the need for user-centered design approaches in algorithm development and evaluation by emphasizing the significance of taking user preferences and practical requirements into account when evaluating the effectiveness of pitch detection algorithms. 1. To develop a hybrid approach combining the Pitch Estimation Filter (PEF) and Cepstrum Pitch Determination (CPD) methods for pitch detection in audio signals. 2. To conduct comparative analysis with existing pitch detection methodologies, including Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics (SRH) and Cepstrum Pitch Determination (CEP), to assess the performance and accuracy of the proposed hybrid approach. 3. To evaluate the effectiveness of the hybrid approach in various real-world applications such as speech recognition and music transcription, using performance metrics including Gross Pitch Error (GPE) and classification accuracy through a K-Nearest Neighbors (KNN) classifier. Proposed PEF+CEP Finally, a comparison and analysis of different pitch detection techniques revealed how well they performed in terms of important evaluation metrics like accuracy, specificity, sensitivity, and gross pitch error (GPE). Conventional methods such as Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics(SRH) and Cepstrum Pitch Determination (CEP) perform admirably in terms of specificity and accuracy, but they are not very effective in terms of sensitivity and GPE. On the other hand, the suggested hybrid approach, Proposed PEF+CEP, offers a noteworthy enhancement in accuracy, attaining a remarkable 98.8%, in addition to a sensitivity of 99.2%. The hybrid approach exhibits a slightly higher GPE than some traditional methods, but these minor deviations are outweighed by the significant improvements in accuracy and sensitivity that it offers. Furthermore, the Proposed PEF+CEP method is a promising solution for reliable and accurate pitch detection in speech processing applications because it strikes a strong balance between computational efficiency, training time, model size, and convergence rate. The suggested method offers notable improvements in pitch detection accuracy and reliability while addressing the drawbacks of separate approaches by utilizing the advantages of both PEF and CEP techniques. As a result, the suggested PEF+CEP approach stands out as a significant advancement in speech processing, offering enhanced functionality and versatility in a range of real-world settings. Finally, a comparison and analysis of different pitch detection techniques revealed how well they performed in terms of important evaluation metrics like accuracy, specificity, sensitivity, and gross pitch error (GPE). Conventional methods such as Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log-Harmonic Summation (LHS), Summation of Residual Harmonics(SRH) and Cepstrum Pitch Determination (CEP) perform admirably in terms of specificity and accuracy, but they are not very effective in terms of sensitivity and GPE. On the other hand, the suggested hybrid approach, Proposed PEF+CEP, offers a noteworthy enhancement in accuracy, attaining a remarkable 98.8%, in addition to a sensitivity of 99.2%. The hybrid approach exhibits a slightly higher GPE than some traditional methods, but these minor deviations are outweighed by the significant improvements in accuracy and sensitivity that it offers. Furthermore, the Proposed PEF+CEP method is a promising solution for reliable and accurate pitch detection in speech processing applications because it strikes a strong balance between computational efficiency, training time, model size, and convergence rate. The suggested method offers notable improvements in pitch detection accuracy and reliability while addressing the drawbacks of separate approaches by utilizing the advantages of both PEF and CEP techniques. As a result, the suggested PEF+CEP approach stands out as a significant advancement in speech processing, offering enhanced functionality and versatility in a range of real-world settings. Pitch detection algorithms could become even more complex and effective with more research and development in this area, enabling improvements in text-to-speech synthesis, speaker Identification, And Speech Recognition, Among Other Fields. Nil