[HN Gopher] Build Audio Search with Vggish
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Build Audio Search with Vggish
Author : todsacerdoti
Score : 29 points
Date : 2021-04-05 13:14 UTC (9 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| ameminator wrote:
| Only if you can pronounce "Vggish"
| varispeed wrote:
| Is there an explanation what exactly does it do? It seems like
| this is searching audio, but based on what and what audio?
| gk1 wrote:
| It's using similarity search[0]. You transform audio files into
| vectors using some embedding model like vggish[1], then index
| those vectors. Then turn the query audio file into a vector the
| same way, and search the index for vectors _similar_ to your
| query.
|
| Full disclosure: I work for Pinecone, which provides similarity
| search as a service... Including for audio data.
|
| [0] https://www.pinecone.io/learn/what-is-similarity-search/
|
| [1] https://tfhub.dev/google/vggish/1
| willseth wrote:
| Can you comment (either generally or wrt your product) on
| specificity/generality/constraints when searching for audio?
| The image-based examples are intuitive, but what features can
| one search for in audio? i.e. Can it detect a specific
| singer's voice, or an instrument or effect? Only more general
| features like genre, tempo, etc? I assume it's some
| combination, but it's not clear to me what this type of
| neural search is "good at".
| rkt08 wrote:
| Looking at the VGGish paper itself, I see they use
| spectrograms as inputs, they show results where they can
| identify instrument types. I'm not too sure how specific
| embeddings from these models can be. Do we know if
| spectrograms can differentiate between two people's voice?
| wfhpw wrote:
| Seems to be based on features generated using a model similar
| to that proposed in [1].
|
| [1] https://research.google/pubs/pub45611/
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(page generated 2021-04-05 23:02 UTC)