A method of training a device specific cloud-based audio processor includes receiving sensor data captured from multiple sensors at a local device. The method also includes receiving spatial information labels computed on the local device using local configuration information. The spatial information labels are associated with the captured sensor data. Lower layers of a first neural network are trained based on the spatial information labels and sensor data. The trained lower layers are incorporated into a second, larger neural network for audio classification. The second, larger neural network may be retrained using the trained lower layers of the first neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a device specific cloud-based audio processor, comprising: receiving sensor data captured from multiple sensors at a local device; receiving spatial information labels computed on the local device using local configuration information, the spatial information labels associated with the captured sensor data; training lower layers of a first neural network based on the spatial information labels and sensor data; incorporating the trained lower layers into a second neural network for audio classification; and retraining the second neural network using the trained lower layers of the first neural network. 2. The method of claim 1, in which retraining comprises retraining the first neural network and the second neural network. 3. The method of claim 1, in which retraining comprises retraining only the second neural network. 4. The method of claim 1, further comprising separating beamformed streams from the sensor data based on the spatial information labels. 5. The method of claim 4, further comprising classifying the sensor data based on the beamformed streams. 6. The method of claim 1, in which the spatial information labels comprise direction of arrival labels. 7. A method of cloud-based audio processing using an artificial neural network, comprising: receiving device identification information of a local device and sensor data captured from multiple sensors at the local device; setting convolutional filters of the neural network based on the device identification information; and predicting an audio event classification based on the sensor data without retraining the neural network. 8. The method of claim 7, further comprising: receiving beamforming filters of the local device; and replacing the convolutional filters of the neural network with the received beamforming filters without retraining the neural network. 9. An apparatus for training a device specific cloud-based audio processor, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured: to receive sensor data captured from multiple sensors at a local device; to receive spatial information labels computed on the local device using local configuration information, the spatial information labels associated with the captured sensor data; to train lower layers of a first neural network based on the spatial information labels and sensor data; to incorporate the trained lower layers into a second neural network for audio classification; and to retrain the second neural network using the trained lower layers of the first neural network. 10. The apparatus of claim 9, in which the at least one processor is further configured to retrain the first neural network and the second neural network. 11. The apparatus of claim 9, in which the at least one processor is further configured to retrain only the second neural network. 12. The apparatus of claim 9, in which the at least one processor is further configured to separate beamformed streams from the sensor data based on the spatial information labels. 13. The apparatus of claim 12, in which the at least one processor is further configured to classify the sensor data based on the beamformed streams. 14. The apparatus of claim 9, in which the spatial information labels comprise direction of arrival labels. 15. An apparatus for training a device specific cloud-based audio processor, comprising: means for receiving sensor data captured from multiple sensors at a local device; means for receiving spatial information labels computed on the local device using local configuration information, the spatial information labels associated with the captured sensor data; means for training lower layers of a first neural network based on the spatial information labels and sensor data; means for incorporating the trained lower layers into a second neural network for audio classification; and means for retraining the second neural network using the trained lower layers of the first neural network. 16. The apparatus of claim 15, in which the means for retraining retrains the first neural network and the second neural network. 17. The apparatus of claim 15, in which the means for retraining retrains only the second neural network. 18. The apparatus of claim 15, further comprising means for separating beamformed streams from the sensor data based on the spatial information labels. 19. The apparatus of claim 18, further comprising means for classifying the sensor data based on the beamformed streams. 20. The apparatus of claim 15, in which the spatial information labels comprise direction of arrival labels.