| General information | 
    
    | Label | Value | Description | 
    
        
    
    |  | Name | TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Development Datasets | Full dataset name | 
    
    
        
    
    |  | ID | sounds/tau_spatial_events_2019_dev | Datalist id for external indexing | 
    
    
        
    
    |  | Abbreviation | TAU-SELD2019-DEV | Official dataset abbreviation, e.g. one used in the original paper | 
    
    
        
    
    |  | Provider | TAU |  | 
    
    
        
    
    |  | Year | 2019 | Dataset release year | 
    
    
        
    
    |  | Modalities | Audio | Data modalities included in the dataset | 
    
    
        
    
    |  | Collection name | TAU Spatial Sound Events | Common name for all related datasets, used to group datasets coming from same source | 
    
    
        
    
    |  | Research domain | SED
        
             SELD
        
             Synthetic
        
             Strong annotation
        
             Multi-channel | Related domains, e.g., Scenes, Mobile devices, Audio-visual, Open set, Ambient noise, Unlabelled, Multiple sensors, SED, SELD, Tagging, FL, Strong annotation, Weak annotation, Unlabelled, Multi-annotator | 
    
    
        
    
    |  | Related datasets name |  |  | 
    
    
        
    
    |  | License | Free |  | 
    
    
        
    
    |  | Download | Download
    
    (7.9GB) |  | 
    
    
    
        
    
    |  | Citation | [Adavanne2019] A multi-room reverberant dataset for sound event localization and detection |  | 
    
                    
    
| Audio | 
    
    | Label | Value | Description | 
    
        
    
    |  | Data | 
        
            
    
    |  |  | Data type | Audio | Possible values: Audio | Features | 
        
        
            
    
    |  |  | File format | 
            
                
    
    |  |  |  | File format type | Constant | Possible values: Constant | Variable | 
            
            
                
    
    |  |  |  | File format | wav | Possible value: wav | aiff | flac | mp3 | aac | ogg | 
            
            
                
    
    |  |  |  | Lossy compression | No | is audio compressed in a lossy manner | 
            
            
            
                
    
    |  |  |  | Sampling rate (kHz) | 48 kHz | Sampling rate in kHz, possible values: 8 | 16 | 22.05 | 32 | 44.1 | 48 | 
            
        
        
            
    
    |  |  | Channels | 
            
                
    
    |  |  |  | Setup | Ambisonic
        
             Array | Possible values: Mono | Stereo | Binaural | Ambisonic | Array | Multi-Channel | Variable | 
            
            
                
    
    |  |  |  | Number of channels | 4 |  | 
            
        
        
            
    
    |  |  | Material | 
            
                
    
    |  |  |  | Source | Original | Possible values: Original | Youtube | Freesound | Online | Crowdsourced | [Dataset name] | 
            
            
                
    
    |  |  |  | Variability sources | Room impulse response
    
            Spatial location | Possible values: Country | City | Location | Position | Device | 
            
        
    
    
        
    
    |  | Content | 
        
            
    
    |  |  | Content type | Synthetic | Possible values: Freefield | Synthetic | Isolated | 
        
        
            
    
    |  |  | Scene | Constant | Is the scene class constant for single recording, possible values: Constant | Variable | 
        
        
            
                
    
    |  |  | Event / Spatial location | Constant | Possible values: Constant | Moving | Unknown | 
            
            
        
    
    
        
    
    |  | Recording | 
        
            
    
    |  |  | Setup | Unknown | Possible values: Near-field | Far-field | Mixed | Uncontrolled | Unknown | 
        
        
            
    
    |  |  | Setup count | 1 | Amount of different recording setups (microphone and recording device) used | 
        
        
            
    
    |  |  | Spot type | Fixed | Possible values: Fixed | Moving | Unknown | 
        
    
    
        
    
    |  | Files | 
        
            
    
    |  |  | Count | 400 files | Total number of files | 
        
        
            
    
    |  |  | Total duration (minutes) | 400 min | Total duration of the dataset in minutes | 
        
        
            
    
    |  |  | File length | Constant | Characterization of the file lengths, possible values: Constant | Quasi-constant | Variable | 
        
        
            
    
    |  |  | File length (seconds) | 60 sec | Approximate length of files | 
        
    
                    
    
| Meta | 
    
    | Label | Value | Description | 
    
        
    
    |  | Types | Event
        
             Spatial location | List of meta data types provided for the data, possible values: Event, Tag, Scene, Caption, Geolocation, Spatial location, Annotator, Timestamp, Presence, Proximity, etc. | 
    
    
        
    
    |  | Scene | 
        
            
    
    |  |  | Classes | 5 | Number of scene classes | 
        
        
            
    
    |  |  | Classes | True | Possible values: True | False | Almost | 
        
        
            
    
    |  |  | Classes | 
    
            Indoor corridor Indoor cafeteria Indoor common area  |  | 
        
        
        
        
    
    
        
    
    |  | Event | 
        
            
    
    |  |  | Classes | 11 | Number of event classes | 
        
        
            
    
    |  |  | Classes | True | Possible values: True | False | Almost | 
        
        
        
            
    
    |  |  | Annotation | 
            
                
    
    |  |  |  | Type | Strong | Possible values: Strong | Weak | Location | None | 
            
            
                
    
    |  |  |  | Source | Synthetic | Possible values: Experts | Crowdsourced | Synthetic | Metadata | Automatic | 
            
            
                
    
    |  |  |  | Annotations per item | 1 | How many annotations there are available per item (possible multi-annotator setup) | 
            
            
                
    
    |  |  |  | Labelled amount (%) | 100 % | Percentage of all data, amount of data which is labelled | 
            
            
            
                
    
    |  |  |  | Strong annotations amount (%) | 100 % | Percentage of all data, amount of data which has strong annotations | 
            
            
                
    
    |  |  |  | Overlapping event instances | Yes |  | 
            
        
        
            
    
    |  |  | Labeling | 
            
                
    
    |  |  |  | Hierarchical | No |  | 
            
            
        
        
            
    
    |  |  | Instance | 
            
            
        
    
    
    
                    
    
| Cross-validation setup | 
    
    | Label | Value | Description | 
    
        
    
    |  |  | Provided | Yes |  | 
    
    
        
    
    |  |  | Folds | 4 |  | 
    
    
        
    
    |  |  | Sets | Train
    
            Test | Set types provided in the split, possible values: Train | Test | Val | Dev | Eval | 
    
                    
    
| Baseline | 
    
    | Label | Value | Description | 
    
        
    
    |  |  | Download | Download | Link to baseline system source code | 
    
    
        
    
    |  |  | Citation | [Adavanne2018] | Paper to cite for the baseline | 
    
                    
    
| Info | 
    
    | Label | Value | Description | 
    
        
    
    |  |  | Evaluation campaign | DCASE2019 task3 | Evaluation campaigns where the dataset was used. |