Update README.md

README.md

@@ -41,62 +41,62 @@ We offer a static set of evaluation datasets and a varied collection of training
 [10]: https://xeno-canto.org
 
 - We assemble a training dataset for each test dataset that is a subset of a complete XC snapshot. We extract all recordings that have vocalizations of the bird species appearing in the test dataset.
+- We use the .ogg format for every recording and a sampling rate of 32 kHz.
 - Each sample in the training dataset is a recording may have more than one vocalization of the corresponding bird species.
-- We omit all recordings from XC that are CC-ND.
+- Each recording in the training datasets has a unique recordist and the corresponding license from XC. We omit all recordings from XC that are CC-ND.
+- The bird species are translated to ebird_codes
 - Snapshot date of XC: 03/10/2024
-- 
+
 ##### Train
 - Exclusively using focal audio data from Xeno-Canto (XC) with quality ratings A, B, C and excluding all recordings that are CC-ND.
-- Each dataset is tailored for specific target species identified in soundscape files.
+- Each dataset is tailored for specific target species identified in the corresponding test soundscape files.
+- We transform the scientific names of the birds into the corresponding ebird_code label. 
 - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
-- We provide the full recordings from XC! These can generate multiple samples from a single instance.
-
-##### Test
-- Only soundscape data sourced from Zenodo.
-- We provide the full recording with the complete label set and specified bounding boxes.
-- This dataset excludes recordings that do not contain bird calls ("no_call").
-- Task: Multiclass ("ebird_code")
+- We provide the full recordings from XC. These can generate multiple samples from a single instance.
 
 ##### Test_5s
+- Task: Multilabel ("ebird_code_multilabel")
 - Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
 - Each recording is segmented into 5-second intervals without overlaps.
-- This contains the "no_call" class.
-- Task: Multilabel ("ebird_code_multilabel")
+- This contains segments without any labels which results in a [0] vector.
 
-#### Subsets
-
-Numbers need to be updated
+##### Test
+- Task: Multiclass ("ebird_code")
+- Only soundscape data sourced from Zenodo.
+- We provide the full recording with the complete label set and specified bounding boxes.
+- This dataset excludes recordings that do not contain bird calls ("no_call").
 
 
-#### FEATURES
+#### Metadata
+
+|                        | format: datasets.                                      |    description           | 
+|------------------------|---------:----------------------------------------------|-------------------------:|
+| audio                  |  Audio(sampling_rate=32_000, mono=True, decode=True)   |     xxxxxx               |  
+| filepath               |  Value("string")                                       |     xxxxxx               |  
+| start_time             |  Value("float64")                                      |     xxxxxx               | 
+| end_time               |  Value("float64")                                      |     xxxxxx               | 
+| low_freq               |  Value("int64")                                        |     xxxxxx               |     
+| high_freq              |  Value("int64")                                        |     xxxxxx               |   
+| ebird_code             |  ClassLabel(names=class_list)                          |     xxxxxx               |  
+| ebird_code_multilabel  |  Sequence(datasets.ClassLabel(names=class_list))       |     x                    |  
+| call_type              |  Sequence(datasets.Value("string"))                    |       x                  |     
+| sex                    |  Value("string")                                       |       x                  |    
+| lat                    |  Value("float64")                                      |       x                  |     
+| long                   |  Value("float64")                                      |       x                  |   
+| length                 |  Value("int64")                                        |       x                  |     
+| microphone             |  Value("string")                                       |       x                  |    
+| license                |  Value("string")                                       |       x                  |     
+| source                 |  Value("string")                                       |       x                  |    
+| local_time             |  Value("string")                                       |       x                  |    
+| detected_events        |  Sequence(datasets.Sequence(datasets.Value("float64")))|       x                  |    
+| event_cluster          |  Sequence(datasets.Value("int64"))                     |       x                  |    
+| peaks                  |  Sequence(datasets.Value("float64"))                   |       x                  |     
+| quality                |  Value("string")                                       |       x                  |     
+| recordist              |  Value("string")                                       |       x                  |     
 
-```python
-{
-    "audio": datasets.Audio(sampling_rate=32_000, mono=True, decode=True),
-    "filepath": datasets.Value("string"),
-    "start_time": datasets.Value("float64"),  # can be changed to timestamp later
-    "end_time": datasets.Value("float64"),
-    "low_freq": datasets.Value("int64"),
-    "high_freq": datasets.Value("int64"),
-    "ebird_code": datasets.ClassLabel(names=class_list),
-    "ebird_code_multilabel": datasets.Sequence(datasets.ClassLabel(names=class_list)),
-    "ebird_code_secondary": datasets.Sequence(datasets.Value("string")),
-    "call_type": datasets.Value("string"),
-    "sex": datasets.Value("string"),
-    "lat": datasets.Value("float64"),
-    "long": datasets.Value("float64"),
-    "length": datasets.Value("int64"),
-    "microphone": datasets.Value("string"),
-    "license": datasets.Value("string"),
-    "source": datasets.Value("string"),
-    "local_time": datasets.Value("string"),
-    "detected_events": datasets.Sequence(datasets.Sequence(datasets.Value("float64"))),
-    "event_cluster": datasets.Sequence(datasets.Value("int64")),
-    "peaks": datasets.Sequence(datasets.Value("float64")),
-    "quality": datasets.Value("string"),
-    "recordist": datasets.Value("string"),
-        })
 ```
+##### Example Metadata Train
+
 ```python
 EXAMPLE TRAIN
 {'audio': {'path': '.ogg',
@@ -135,7 +135,8 @@ EXAMPLE TRAIN
  'quality': 'A',
  'recordist': '...'}
 
-EXAMPLE TEST_5S
+##### Example Metadata Test5s
+
 {'audio': {'path': '.ogg',
   'array': array([-0.67190468, -0.9638235 , -0.99569213, ..., -0.01262935,
          -0.01533066, -0.0141047 ]),
@@ -146,7 +147,7 @@ EXAMPLE TEST_5S
  'low_freq': 0,
  'high_freq': 3098,
  'ebird_code': None,
- 'ebird_code_multilabel': [],
+ 'ebird_code_multilabel': [1, 10],
  'ebird_code_secondary': None,
  'call_type': None,
  'sex': None,