A naukri.com initiative
Medium
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Gender Classification from Voice: A Deep Learning Approach with CNN and Mel Spectrograms
- This project focused on developing a deep learning system to classify gender from voice samples using Convolutional Neural Networks (CNN) and mel spectrograms.
- By interpreting mel spectrograms, the CNN was able to identify differences in how male and female voices behave in frequency and time.
- The project aimed to construct a robust gender classification model through data preparation, feature extraction, CNN training, and performance evaluation.
- Challenges were encountered when working with real-world audio data, emphasizing the complexities of deep learning models.
- Two datasets were utilized, and various audio augmentations were applied during training for improved model generalization.
- Principal Component Analysis (PCA) was considered but found to be unsuitable for audio classification tasks using CNNs due to its limitations.
- CNNs trained on spectrograms learn task-specific features focusing on time and frequency relationships critical in speech data analysis.
- Spectrograms offer visual interpretability compared to abstract PCA components, aiding in understanding pitch, formants, and energy in the signal.
- Instead of PCA, the CNN directly learned from high-resolution spectrograms, while applying regularization techniques to mitigate overfitting.
- This study highlighted that gender classification from voice involves nuanced patterns beyond pitch, effectively tackled by modern deep learning techniques.
- The system achieved over 93% accuracy and demonstrated reliable performance on real-world audio data, offering potential for further exploration in voice analysis.
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