Blog: upload the now-playing blog

Author: abhibongale

app/blog/posts/now-playing-implementation.mdx

@@ -1,5 +1,5 @@
 ---
-title: now-playing-implementation
+title: Now-Playing Research Paper: Streamlined Implementation
 publishedAt: '2024-05-26'
 summary: 'Streamlined implementation of the now-playing research paper'
 tags:

app/blog/posts/now-playing.mdx

@@ -1,5 +1,5 @@
 ---
-title: now-playing
+title: Now-Playing Research Paper: Summarisation of the paper
 publishedAt: '2025-05-26'
 summary: 'Brief summarisation about the Now Playing research paper'
 tags:
@@ -31,7 +31,7 @@ This paper presents the music recognizer build using combining **state-of-the-ar
 * The neural network fingerprinter (NNF)  job is to analyze a few seconds of audio and emits a single embedding at a rate of one per second. 
 * The neural network fingerprinter architecture consist of Convolutions and divide-and-encode layers (full connected layers).
 * The Architecture:
-![Pasted image 20250514220504.png](/posts/assets/now-playing-arch.png "Image Ref: Now Playing: Continuous low-power music recognition")
+![now-playing-arch](public/assets/now-playing-arch.png)
 		*Image Ref: [Now Playing: Continuous low-power music recognition](https://arxiv.org/abs/1711.10958)*
  * All layers except for the final divide-and-encode layer use the ELU activation function and batch normalization.
  * The network is trained using the triplet loss function, which uses three audio segments  anchor positive audio segment, negative audio segment, we tried to minimize the anchor and positive audio segment distance while increasing distance of anchor and negative audio segment.

next.config.js

@@ -3,6 +3,10 @@ import NextConfig from 'next';
 const nextConfig = {
   output: 'export',
   basePath: process.env.PAGES_BASE_PATH,
+  assetPrefix: process.env.PAGES_BASE_PATH,
+  images: {
+    unoptimized: true, // Allow images to be unoptimized when deploying on GitHub Pages
+  },
 };
 
 export default nextConfig;