Exploring audio data augmentation – Labeling Audio Data-2
Zavia Dunlap
By using this noise-augmented audio data, the model accuracy increased from 0.946 to 0.964. Depending on the data, we can apply data augmentation and test the accuracy to decide whether…
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Spectrogram visualization
Waveform visualization
Zero-crossing rate
Step 3 – setting up FFmpeg – Labeling Audio Data
Whisper is designed to transcribe audio, but it requires a specific format for processing. The format required by Whisper for processing audio is WAV format. Whisper is designed to transcribe…
Transcribing audio using the OpenAI Whisper model – Labeling Audio Data
In this section, we are going to see how to transcribe audio file to text using the OpenAI Whisper model and then label the audio transcription using the OpenAI large…
Real-time voice classification with Random Forest – Labeling Audio Data-2
Combine and shuffle data: Positive and negative samples are combined into feature vectors (X) and corresponding labels (y). The data is shuffled to ensure a balanced distribution during training: Combine…
Real-time voice classification with Random Forest – Labeling Audio Data-1
Downloading FFmpeg FFmpeg is a versatile and open source multimedia framework that facilitates the handling, conversion, and manipulation of audio and video files (https://ffmpeg.org/download.html). To download FFmpeg for macOS, select…
Troubleshooting common installation issues for audio libraries – Exploring Audio Data
Here are some troubleshooting steps for common installation issues related to Librosa and other commonly used audio libraries in Python: Troubleshooting steps: Troubleshooting steps: Troubleshooting steps: Troubleshooting steps: Troubleshooting steps:…
Ethical implications of audio data – Exploring Audio Data
Considerations for visualizations Multimodal integration: Visualizations can be combined with other modalities (text, image) for multimodal analysis, enhancing the understanding of audio data in various contexts. Real-time applications: Some visualizations…
Spectrogram visualization – Exploring Audio Data
A spectrogram is a more advanced visualization that shows how the audio’s frequency content changes over time. It’s like a heat map, where different colors represent different frequencies: Generate a…
Zero-crossing rate – Exploring Audio Data
The zero-crossing rate measures how rapidly the signal changes from positive to negative or vice versa. It’s often used to characterize noisiness in audio. Here’s how you can calculate it:…