Fixing references

README.md

@@ -13,6 +13,11 @@ tags:
 pretty_name: OpenPIR
 size_categories:
   - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: openpir_metadata.jsonl
 ---
 
 # OpenPIR: Open Predominant Instrument Recognition Dataset
@@ -109,13 +114,15 @@ OpenPIR was used to fine-tune a baseline PIR model (trained on IRMAS + MUMS solo
 
 ### Comparison with prior work
 
-Adding OpenPIR (≈3.2 hours of additional data) achieves competitive Micro and Macro F1 against systems that use substantially more augmentation data.
+Adding OpenPIR (≈3.2 hours of additional data) achieves competitive Micro and Macro F1 against systems that use substantially more augmentation data. Reference numbers correspond to the [References](#references) section below.
 
 ![SOTA comparison table](OpenPIR_PerformanceStat_SOTA.png)
 
-### Per-class performance vs. Han et al. baseline
+¹ Yu et al. results reproduced from the original paper; evaluated on a different test split.
 
-The chart below compares per-class Precision, Recall, and F1 for our best model against a reimplementation of Han et al.
+### Per-class performance vs. Han et al. \[2\]
+
+The chart below compares per-class Precision, Recall, and F1 for our best model (Model C) against a reimplementation of Han et al.
 
 ![Per-class precision, recall, and F1 comparison](OpenPIR_PerformanceStat_Han.png)
 
@@ -231,6 +238,29 @@ and OpenMic-2018:
 
 ---
 
+## References
+
+References cited in the SOTA comparison table, renumbered [1]–[8]:
+
+\[1\] J. J. Bosch, J. Janer, F. Fuhrmann, and P. Herrera. "A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals." *ISMIR*, 2012.
+
+\[2\] Y. Han, J. Kim, and K. Lee. "Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music." *IEEE/ACM Trans. Audio, Speech, Lang. Process.*, 2017.
+
+\[3\] J. Pons, O. Slizovskaia, R. Gong, E. Gómez, and X. Serra. "Timbre Analysis of Music Audio Signals with Convolutional Neural Networks." *EUSIPCO*, 2017.
+
+\[4\] K. Avramidis, A. Kratimenos, C. Garoufis, and P. Maragos. "Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio." *ICASSP*, 2021.
+
+\[5\] D. Yu, H. Wang, P. Chen, and Z. Wei. "Predominant Instrument Recognition Based on Deep Neural Network with Auxiliary Classification." *IEEE/ACM Trans. Audio, Speech, Lang. Process.* 28 (2020), pp. 852–861.
+
+\[6\] A. Kratimenos, K. Avramidis, M. Kosta, and M. Kokiopoulou. "Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music." *EUSIPCO*, 2021.
+
+\[7\] L. C. Reghunath and R. Rajan. "Transformer-Based Ensemble Method for Multiple Predominant Instruments Recognition in Polyphonic Music." *EURASIP J. Audio, Speech, Music Process.* 2022.1 (2022), p. 11.
+
+\[8\] L. Zhong, Z. Chen, W. Liang, J. Li, and C. Shi. "Exploring Isolated Musical Notes as Pre-Training Data for Predominant Instrument Recognition in Polyphonic Music." *APSIPA ASC*, 2023, pp. 2312–2319.
+
+---
+
 ## Contact
 
-Charis Cochran — [crc356 [at] drexel.edu]  
+Charis Cochran — crc356 [at] drexel [dot] edu  
+Drexel University / University of Pennsylvania