From 55b898daedb0858434e3a750133b9d1d1106ea75 Mon Sep 17 00:00:00 2001
From: Dipam Chakraborty <dipamc77@gmail.com>
Date: Thu, 1 Dec 2022 16:32:15 +0530
Subject: [PATCH] test latex

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 2ebdd5d..dda27bd 100644
--- a/README.md
+++ b/README.md
@@ -61,19 +61,19 @@ For both the leaderboards, the winning teams will be required to publish their t
 
 As an evaluation metric, we are using the signal-to-distortion ratio (SDR), which is defined as,
 
-![](https://images.aicrowd.com/uploads/ckeditor/pictures/404/content_SDR_instr.png)
+$SDR_{instr} = 10log_{10}\frac{\sum_n(s_{instr,left\ channel}(n))^2 + \sum_n(s_{instr,right\ channel}(n))^2}{\sum_n(s_{instr,left\ channel}(n) - \hat{s}_{instr,left\ channel}(n))^2 + \sum_n(s_{instr,right\ channel}(n) - \hat{s}_{instr,right\ channel}(n))^2}$
 
-where S𝑖𝑛𝑠𝑡𝑟(n) is the waveform of the ground truth and Ŝ𝑖𝑛𝑠𝑡𝑟(𝑛) denotes the waveform of the estimate. The higher the SDR score, the better the output of the system is.
+where $S_{instr}(n)$ is the waveform of the ground truth and Ŝ𝑖𝑛𝑠𝑡𝑟(𝑛) denotes the waveform of the estimate. The higher the SDR score, the better the output of the system is.
 
 In order to rank systems, we will use the average SDR computed by
 
-![](https://images.aicrowd.com/uploads/ckeditor/pictures/405/content_SDR_song.png)
+$SDR_{song} = \frac{1}{4}(SDR_{bass} + SDR_{drums} + SDR_{vocals} + SDR_{other})$
 
 for each song. Finally, the overall score is obtained by averaging SDRsong over all songs in the hidden test set.
 
 # Baselines
 
-TODO: To be added
+We use Open-Unmix for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the openunmix-baseline branch on this repository. To test the models locally, you need to install `git-lfs`.
 
 # How to Test and Debug Locally
 
-- 
GitLab