From 4b7c828a4527268b5a5479fc234939769deb1002 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:28:04 +0530 Subject: [PATCH 01/14] Update README.md --- docs/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/README.md b/docs/README.md index a46f4d6..aaf9746 100644 --- a/docs/README.md +++ b/docs/README.md @@ -38,14 +38,14 @@ title: ## Abstract Music is a powerful language that stirs up a wide range of emotions in people. Understanding and recognizing these emotions is crucial for applications like personalized music recommendations, music therapy, and affective computing. Music Emotion Recognition (MER) systems aim to automatically analyze and classify the emotional content of music, providing insights into how different compositions affect our emotions. -#### Preliminary Knowledge +##### Preliminary Knowledge Existing MER publications can be mainly categorized into two sections, namely song-level MER (or static) and music emotion variation detection (MEVD, or dynamic). Assigning the overall emotion label (or labels, if the task is viewed as a multi-classification or regression problem) to one song is referred to as song-level MER.MEVD considers the emotion of the music as a changing process, where the dynamic changing process of emotion needs to be recognized when conducting MEVD research. -#### Research framework: +##### Research framework: Most existing MER works based on machine learning include three parts and they are, domain definition, feature extraction, and emotion recognition. “Figure. 1”, shows the overall framework of MER systems. -#### Emotion Model: +##### Emotion Model: Dimensional emotion models are widely used in MER systems. There are two main dimension emotion models namely Thayer’s emotion model and Russell’s circumplex model. Both models use arousal and valence values (AV values) to identify the emotion in a given music sample. Associated with the AV values, each music sample becomes a point in the arousal-valence plane, so the users can efficiently retrieve the music sample by specifying a desired point in the emotion plane."Figure. 2" shows Thayer’s emotion model associated with MER. | Figure 1 | Figure 2 | From 49889623b1a135e919c7e5157d43d4b5db58cd12 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:33:46 +0530 Subject: [PATCH 02/14] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index aaf9746..4ad0f88 100644 --- a/docs/README.md +++ b/docs/README.md @@ -106,7 +106,7 @@ We applied different layers of DNN with different number of units to test to imp | Best Model Developed VsExsisting models (RMSE) | | ------- | -| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_1.png) | +| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_2.png) | | Best Model Developed VsExsisting models (R2 Score) | | ------- | From 66ad0b8e165933c9102a827ff069c4c7c8d15354 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:35:04 +0530 Subject: [PATCH 03/14] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 4ad0f88..aaf9746 100644 --- a/docs/README.md +++ b/docs/README.md @@ -106,7 +106,7 @@ We applied different layers of DNN with different number of units to test to imp | Best Model Developed VsExsisting models (RMSE) | | ------- | -| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_2.png) | +| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_1.png) | | Best Model Developed VsExsisting models (R2 Score) | | ------- | From 71f6e8403748b2a649dbc474cf5dc793a8b17b0c Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:35:53 +0530 Subject: [PATCH 04/14] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index aaf9746..83f0b09 100644 --- a/docs/README.md +++ b/docs/README.md @@ -106,7 +106,7 @@ We applied different layers of DNN with different number of units to test to imp | Best Model Developed VsExsisting models (RMSE) | | ------- | -| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_1.png) | +| ![Image 9](./images/best_model_developed_Vs_Exsisting_Model_1.jpg) | | Best Model Developed VsExsisting models (R2 Score) | | ------- | From 4db59a85b7ed117812a401e3bf9d58668e9b4cd0 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:52:18 +0530 Subject: [PATCH 05/14] Update README.md --- docs/README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/README.md b/docs/README.md index 83f0b09..b2990ef 100644 --- a/docs/README.md +++ b/docs/README.md @@ -116,6 +116,14 @@ We applied different layers of DNN with different number of units to test to imp | ------- | | ![Image 11](./images/best_model_developed_Vs_Exsisting_Model_3.png) | +## Results and Analysis + +##### Prediction System + +The below graph visually represents the dynamic emotional trajectory of a music track over its duration. Time is plotted on the x-axis, segmented into 2-second intervals, covering the entire length of the song. The y-axis reflects the predicted emotional states, showcasing how the emotions of the music evolve over time. Each point on the graph corresponds to a 2-second segment of the song, with the connected line illustrating the flow and transitions between different predicted emotions. Peaks and valleys in the graph indicate significant shifts in emotional content, providing a comprehensive and insightful overview of the emotional journey experienced throughout the entire musical composition. + +| ![Image 12](./images/best_model_developed_Vs_Exsisting_Model_3.png)| + ## Conclusion
  • Systems that are using DNN, have a higher accuracy compared to the systems that use only the traditional machine learning algorithms.
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with different number of units to test to imp The below graph visually represents the dynamic emotional trajectory of a music track over its duration. Time is plotted on the x-axis, segmented into 2-second intervals, covering the entire length of the song. The y-axis reflects the predicted emotional states, showcasing how the emotions of the music evolve over time. Each point on the graph corresponds to a 2-second segment of the song, with the connected line illustrating the flow and transitions between different predicted emotions. Peaks and valleys in the graph indicate significant shifts in emotional content, providing a comprehensive and insightful overview of the emotional journey experienced throughout the entire musical composition. -| ![Image 12](./images/best_model_developed_Vs_Exsisting_Model_3.png)| +| ![Image 12](./images/Emotion_graph.png)| + + ## Conclusion From 6538b3208b43931f8f4dee9b2ebd658062ca23d9 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 17:58:39 +0530 Subject: [PATCH 08/14] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index 2e5eee1..2022649 100644 --- a/docs/README.md +++ b/docs/README.md @@ -122,7 +122,7 @@ We applied different layers of DNN with different number of units to test to imp The below graph visually represents the dynamic emotional trajectory of a music track over its duration. Time is plotted on the x-axis, segmented into 2-second intervals, covering the entire length of the song. The y-axis reflects the predicted emotional states, showcasing how the emotions of the music evolve over time. Each point on the graph corresponds to a 2-second segment of the song, with the connected line illustrating the flow and transitions between different predicted emotions. Peaks and valleys in the graph indicate significant shifts in emotional content, providing a comprehensive and insightful overview of the emotional journey experienced throughout the entire musical composition. -| ![Image 12](./images/Emotion_graph.png)| +![Image 12](./images/Emotion_graph.png) From 3d4d7d8f71e79660d58c297d0e371fce00cf9173 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 18:06:19 +0530 Subject: [PATCH 09/14] Update README.md --- docs/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/README.md b/docs/README.md index 2022649..e22666c 100644 --- a/docs/README.md +++ b/docs/README.md @@ -124,6 +124,10 @@ The below graph visually represents the dynamic emotional trajectory of a music ![Image 12](./images/Emotion_graph.png) +Following the detailed emotional exploration, the accompanying pie chart offers a concise summary of the overall emotional composition of the song. Each slice of the pie corresponds to a distinct emotion category, such as happiness, anger, sadness, and calmness. The size of each slice represents the proportion of the song's duration characterized by that particular emotion, offering a quick and intuitive overview of the predominant emotional themes woven into the fabric of the musical experience. + +![Image 13](./images/Emotion_graph.png) + ## Conclusion From 549e67b18c1fbd36735e7d4b3de4e68dac108997 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 18:06:38 +0530 Subject: [PATCH 10/14] Update README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index e22666c..be97407 100644 --- a/docs/README.md +++ b/docs/README.md @@ -118,7 +118,7 @@ We applied different layers of DNN with different number of units to test to imp ## Results and Analysis -##### Prediction System +#### Prediction System The below graph visually represents the dynamic emotional trajectory of a music track over its duration. Time is plotted on the x-axis, segmented into 2-second intervals, covering the entire length of the song. The y-axis reflects the predicted emotional states, showcasing how the emotions of the music evolve over time. Each point on the graph corresponds to a 2-second segment of the song, with the connected line illustrating the flow and transitions between different predicted emotions. Peaks and valleys in the graph indicate significant shifts in emotional content, providing a comprehensive and insightful overview of the emotional journey experienced throughout the entire musical composition. 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README.md --- docs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/README.md b/docs/README.md index be97407..07d8324 100644 --- a/docs/README.md +++ b/docs/README.md @@ -126,7 +126,7 @@ The below graph visually represents the dynamic emotional trajectory of a music Following the detailed emotional exploration, the accompanying pie chart offers a concise summary of the overall emotional composition of the song. Each slice of the pie corresponds to a distinct emotion category, such as happiness, anger, sadness, and calmness. The size of each slice represents the proportion of the song's duration characterized by that particular emotion, offering a quick and intuitive overview of the predominant emotional themes woven into the fabric of the musical experience. -![Image 13](./images/Emotion_graph.png) +![Image 13](./images/Emotion_pie_graph.png) From 6a91439c95ba2449ec7efba29b589c90ca5069de Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 18:17:25 +0530 Subject: [PATCH 13/14] Update README.md --- docs/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/README.md b/docs/README.md index 07d8324..7695064 100644 --- a/docs/README.md +++ b/docs/README.md @@ -38,14 +38,14 @@ title: ## Abstract Music is a powerful language that stirs up a wide range of emotions in people. Understanding and recognizing these emotions is crucial for applications like personalized music recommendations, music therapy, and affective computing. Music Emotion Recognition (MER) systems aim to automatically analyze and classify the emotional content of music, providing insights into how different compositions affect our emotions. -##### Preliminary Knowledge +#### Preliminary Knowledge Existing MER publications can be mainly categorized into two sections, namely song-level MER (or static) and music emotion variation detection (MEVD, or dynamic). Assigning the overall emotion label (or labels, if the task is viewed as a multi-classification or regression problem) to one song is referred to as song-level MER.MEVD considers the emotion of the music as a changing process, where the dynamic changing process of emotion needs to be recognized when conducting MEVD research. -##### Research framework: +#### Research framework: Most existing MER works based on machine learning include three parts and they are, domain definition, feature extraction, and emotion recognition. “Figure. 1”, shows the overall framework of MER systems. -##### Emotion Model: +#### Emotion Model: Dimensional emotion models are widely used in MER systems. There are two main dimension emotion models namely Thayer’s emotion model and Russell’s circumplex model. Both models use arousal and valence values (AV values) to identify the emotion in a given music sample. Associated with the AV values, each music sample becomes a point in the arousal-valence plane, so the users can efficiently retrieve the music sample by specifying a desired point in the emotion plane."Figure. 2" shows Thayer’s emotion model associated with MER. | Figure 1 | Figure 2 | From f95a208e8a4ab807eef03fc9272244ee815a10f6 Mon Sep 17 00:00:00 2001 From: Madush Chandrasena <86040480+MadushDilshan@users.noreply.github.com> Date: Fri, 10 Nov 2023 18:24:21 +0530 Subject: [PATCH 14/14] Update README.md --- docs/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/README.md b/docs/README.md index 7695064..ca5e2b8 100644 --- a/docs/README.md +++ b/docs/README.md @@ -128,6 +128,8 @@ Following the detailed emotional exploration, the accompanying pie chart offers ![Image 13](./images/Emotion_pie_graph.png) +The song exhibits a range of emotions, with a notable section that is depressing, suggesting that the theme could be mournful or very emotional. The song's majority is made up of calm, which offers quiet and muted intense moments. Anger and delight also play a part, albeit in smaller amounts, in creating a complex and emotionally charged musical experience. The song's music is intricate and emotionally compelling, as it seems to move through a variety of emotions overall. + ## Conclusion