diff --git a/tabs/final_report.md b/tabs/final_report.md
index 5e11680..feaaf40 100644
--- a/tabs/final_report.md
+++ b/tabs/final_report.md
@@ -550,6 +550,14 @@ However, it may well be that spectrograms still provide valuable insights, as th
 
 Perhaps one of the most interesting insights we find is in how the model does its missclassifications, a finding that is more pronounced in the poorly-performing models. Namely, it is the interesting consequence of rock being a genre that has both a number of predecessor genres (blues, bluegrass, country, boogie-woogie, gospel, and country music) as well as successor genres (pop, metal, etc.). This results in a number of misclassifications for rock, being the "central genre" between these predecessors and successors. Additionally, genres that are not part of this interrelated family end up having performance that is quite high, especially evident with classical music. This potentially shows that the models (however medicore some of their individual performances may be) are learning fundamental features that define western music as an art form.
 
+**Overall**:
+
+With our project, we implemented several different architectures, with each model crafted towards a specific representation of music - midi, spectrograms, and extracted features. These models were able to extract information from each representation and perform supervised classification on its genre. 
+
+Midi, as a logical and intuitive way to organize music, make features such as intervals, chords, and progressions much easier to parse. This prompted us to use techniques that can utilize these structures to its fullest - tree based methods. Raw spectrogram files is a way to represent audio files directly that can be learned by a spectrogram. Our work shows that deep convolutions neural network is able to learn complex features and understand genre. However, due to the large dimensionality of audio files, learning features from spectrogram files requires complex models and large datasets. We were able to get better results by using 1D convolutions to account for music's unique representation in the frequency domain. We discovered that human selected features by industry experts performed the best. This reflects the paradigm that domain knowledge can boost machine learning methods by significantly reducing the size and simplicity of models, and can perform complex methods trained on raw data.
+
+Our results explores the capabilities of machine learning methods when applied on supervised learning tasks to different representations of music.
+
 ## Next Steps
 **MusicNet**:
 1. Fine-tuning Hyperparameters: The decision tree provides a baseline, and the hyperparameter search space can be refined based on its results, and we could do more experimentation with random forests and gradient-boosted trees hyperparameters could potentially improve performance.
@@ -560,13 +568,6 @@ Perhaps one of the most interesting insights we find is in how the model does it
 1. Improving Performance with Spectrogram Data: Exploring performance improvement with spectrogram data is a promising avenue. Human-extracted features may not benefit significantly from more complex models, as our work shows high performance but diminishing returns. Spectrograms, containing more information, paired with sophisticated models and better preprocessing techniques, could enhance performance further.
 2. Combining Convolutional Feature Extractor with Human-Extracted Features: A hybrid approach could involve building a model that combines a convolutional feature extractor with human-extracted features. The concatenated features would then be classified by a feedforward network (MLP). This method aims to merge the simplicity of human-derived features with the detailed insights from spectrograms, potentially creating a superior model.
 
-**Overall**:
-With our project, we implemented several different architectures, with each model crafted towards a specific representation of music - midi, spectrograms, and extracted features. These models were able to extract information from each representation and perform supervised classification on its genre. 
-
-Midi, as a logical and intuitive way to organize music, make features such as intervals, chords, and progressions much easier to parse. This prompted us to use techniques that can utilize these structures to its fullest - tree based methods. Raw spectrogram files is a way to represent audio files directly that can be learned by a spectrogram. Our work shows that deep convolutions neural network is able to learn complex features and understand genre. However, due to the large dimensionality of audio files, learning features from spectrogram files requires complex models and large datasets. We were able to get better results by using 1D convolutions to account for music's unique representation in the frequency domain. We discovered that human selected features by industry experts performed the best. This reflects the paradigm that domain knowledge can boost machine learning methods by significantly reducing the size and simplicity of models, and can perform complex methods trained on raw data.
-
-Our results explores the capabilities of machine learning methods when applied on supervised learning tasks to different representations of music.
-
 
 ## Contribution Table