Update README.md

README.md

@@ -31,35 +31,41 @@ size_categories:
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 ### Dataset Description
 
-"Benchmarking embeddings for retrieval and discrimination of vocalizations in humans and songbirds". 
-This bechmarking aggregated dataset consists of a collection of vocalization samples from humans and songbirds.
+>
+> **Benchmarking embeddings for retrieval and discrimination of vocalizations in humans and songbirds**
+> 
+> Maris Basha<sup>1,2</sup>, Sabine Stoll<sup>1</sup>, Richard H. R. Hahnloser<sup>1,2</sup> <br>
+> University of Zurich<sup>1</sup> and ETH Zurich<sup>2</sup>
 
-### Data Fields
+This multi-species dataset was customized to benchmark k-NN retrieval and cluster separation tecniques on Human and Songbird vocalizations.
 
-1. **Subset**: Specifies the subset/category of the dataset. It can indicate whether the sample is from humans or songbirds, and possibly more detailed categorization.
+## Download Dataset
+```python
+from huggingface_hub import snapshot_download
+snapshot_download('songbirdini/vocsim', local_dir = "data/vocsim", repo_type="dataset" )
+```
 
-2. **Audio**: Contains the audio sample.
+For more usage details, please refer to the GitHub repository: https://github.com/marisbasha/neural_embeddings
 
-3. **Label**: Represents the label or class of the audio clip, indicating the type of vocalization or sound.
 
+### Data Fields
+1. **Subset**: Specifies the subset/category of the dataset. It can indicate whether the sample is from humans or songbirds, and possibly more detailed categorization.
+2. **Audio**: Contains the audio sample.
+3. **Label**: Represents the label or class of the audio clip, indicating the type of vocalization or sound.
 4. **Speaker**: Identifies the speaker or source of the vocalization in the case of human datasets, or the individual bird in the case of songbird datasets.
 
 ### Human Datasets
-
-1. **AMI**: The AMI Meeting Corpus comprises 100 hours of multi-modal meeting recordings, including audio data for utterances, words, and vocal sounds, alongside detailed speaker metadata.
-
-2. **TIMIT**: The TIMIT dataset contains manual phonetic transcriptions of utterances read by 630 English speakers with various dialects.
-
-3. **VocImSet**: The Vocal Imitation Set features recordings of 236 unique sound sources being imitated by 248 speakers.
-
+1. [**AMI**](https://groups.inf.ed.ac.uk/ami/corpus/): The AMI Meeting Corpus comprises 100 hours of multi-modal meeting recordings, including audio data for utterances, words, and vocal sounds, alongside detailed speaker metadata.
+2. [**TIMIT**](https://catalog.ldc.upenn.edu/LDC93S1): The TIMIT dataset contains manual phonetic transcriptions of utterances read by 630 English speakers with various dialects.
+3. [**VocImSet**](https://zenodo.org/records/1340763): The Vocal Imitation Set features recordings of 236 unique sound sources being imitated by 248 speakers.
 
 ### Songbird Datasets
 
-1. **Tomka**: The Gold-Standard Zebrafinch dataset contains 48,059 vocalizations of 36 vocalization types from 4 zebra finches.
-
-2. **Nicholson**: The Bengalese finch song repository includes songs of four Bengalese finches recorded in the Sober lab at Emory University and manually clustered by two authors.
-
-3. **DAS**: The Deep Audio Segmenter Dataset features single male Bengalese finch songs, including 473 vocalizations of 6 vocalization types.
+1. [**Tomka**](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/655689/2023.09.04.555475v1.full.pdf): The Gold-Standard Zebrafinch dataset contains 48,059 vocalizations of 36 vocalization types from 4 zebra finches.
+2. [**Nicholson**](https://figshare.com/articles/dataset/Bengalese_Finch_song_repository/4805749/9): The Bengalese finch song repository includes songs of four Bengalese finches recorded in the Sober lab at Emory University and manually clustered by two authors.
+3. [**DAS**](https://elifesciences.org/articles/68837): The Deep Audio Segmenter Dataset features single male Bengalese finch songs, including 473 vocalizations of 6 vocalization types.
+4. [**Elie**](https://figshare.com/articles/dataset/Vocal_repertoires_from_adult_and_chick_male_and_female_zebra_finches_Taeniopygia_guttata_/11905533/1): Vocal repertoires from zebra finches, collected between 2011 and 2014 at the University of California Berkeley by Julie E Elie. This dataset contains 3,500 vocalizations from 50 individuals and 65 vocalization types.
 
-4. **Elie**: Vocal repertoires from zebra finches, collected between 2011 and 2014 at the University of California Berkeley by Julie E Elie. This dataset contains 3,500 vocalizations from 50 individuals and 65 vocalization types.
 
+## Contact
+maris@ini.ethz.ch