Added details on validation set features

README.md

@@ -18,3 +18,17 @@ This is a highly effective dataset for training custom openwakeword models.
 **Dataset source**: https://acav100m.github.io/
 
 **Size**: An array of shape (5625000, 16, 96), corresponding to ~2000 hours of audio from the ACAV100M dataset. Each row in the array has a temporal dimension of 16, which at 80 ms per temporal step results in each row containing features representing 1.28 seconds of audio.
+
+
+## False-Positive Validation Set
+
+This is a hand-selected combination of audio features (representing ~11 hours of total audio) that serves as a false-positive validation set when training custom openWakeWord models.
+It is intended to be broadly representative of the different types of environments where openWakeWord models could be deployed, and thus useful for estimating false-positive rates.
+
+The contributing audio datasets are:
+
+1) The entire [DiPCo](https://www.amazon.science/publications/dipco-dinner-party-corpus) dataset (~5.3 hours)
+2) Selected clips from the [Santa Barbara Corpus of Spoken American English](https://www.linguistics.ucsb.edu/research/santa-barbara-corpus) (~3.7 hours)
+3) Selected clips from the [MUSDB Music Dataset](https://sigsep.github.io/datasets/musdb.html) (2 hours)
+
+Note that the MUSDB audio data was first reverberated with the [MIT impulse response recordings](https://huggingface.co/datasets/davidscripka/MIT_environmental_impulse_responses) to make it more representative of real-world deployments.