Row 85059

Row ID: 85059 | Dataset Entry | Axioma AXP Content Repository

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Thanks for sharing your results! Just a couple of things to clear up. First, our Speaker Identification was done on VoxCeleb1, not VoxCeleb2.

We’re very mindful of overfitting. We’ve used regularization techniques and closely monitored the training/validation loss graphs to keep things in check. Similar results have been reproduced by other researchers as well.

About the spectrogram patches: audio spectrograms are visual representations of the spectrum of frequencies in a sound over time, displayed as a 2D image where one axis represents time and the other frequency. These can be very large depending on the duration and complexity of the audio. Splitting the spectrogram into patches reduces the dimensionality, making each patch a more manageable chunk of data for the model to process. Also, each patch in a spectrogram contains local frequency and time information. This helps the model to learn from and recognize specific patterns in the sound, such as the pitch and rhythm of speech or music. It captures local dependencies, which are crucial for tasks like sound event classification and speech tasks.

Thank you for the discussion. Please let me know if anything needs further clarification.

FieldValue
text Thanks for sharing your results! Just a couple of things to clear up. First, our Speaker Identification was done on VoxCeleb1, not VoxCeleb2. We’re very mindful of overfitting. We’ve used regularization techniques and closely monitored the training/validation loss graphs to keep things in check. Similar results have been reproduced by other researchers as well. About the spectrogram patches: audio spectrograms are visual representations of the spectrum of frequencies in a sound over time, disp…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-24
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Raw Record

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  "text": "Thanks for sharing your results! Just a couple of things to clear up. First, our Speaker Identification was done on VoxCeleb1, not VoxCeleb2.\n\nWe’re very mindful of overfitting. We’ve used regularization techniques and closely monitored the training/validation loss graphs to keep things in check. Similar results have been reproduced by other researchers as well.\n\nAbout the spectrogram patches: audio spectrograms are visual representations of the spectrum of frequencies in a sound over time, displayed as a 2D image where one axis represents time and the other frequency. These can be very large depending on the duration and complexity of the audio. Splitting the spectrogram into patches reduces the dimensionality, making each patch a more manageable chunk of data for the model to process. Also, each patch in a spectrogram contains local frequency and time information. This helps the model to learn from and recognize specific patterns in the sound, such as the pitch and rhythm of speech or music. It captures local dependencies, which are crucial for tasks like sound event classification and speech tasks.\n\nThank you for the discussion. Please let me know if anything needs further clarification.",
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