**AccoMontage** is a model for accompaniment arrangement, a type of music generation task involving intertwined constraints of melody, harmony, texture, and music structure. AccoMontage generates piano accompaniments for folk/pop songs based on a lead sheet (i.e. a melody with chord progression). It first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure deep learning approaches, AccoMontage uses a hybrid pathway, in which rule-based optimization and deep learning are both leveraged.

**SMITH**, or **Siamese Multi-depth Transformer-based Hierarchical Encoder**, is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based model for document representation learning and matching. It contains several design choices to adapt [self-attention models](https://paperswithcode.com/methods/category/attention-modules) for long text inputs. For the model pre-training, a masked sentence block language modeling task is used in addition to the original masked word language model task used in [BERT](https://paperswithcode.com/method/bert), to capture sentence block relations within a document. Given a sequence of sentence block representation, the document level Transformers learn the contextual representation for each sentence block and the final document representation.

SMITH

Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching

AccoMontage

AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer

**SPADE**, or **Spatially-Adaptive Normalization** is a conditional normalization method for semantic image synthesis. Similar to [Batch Normalization](https://www.paperswithcode.com/method/batch-normalization), the activation is normalized in the channel-wise manner and then modulated with learned scale and bias. In the SPADE, the mask is first projected onto an embedding space and then convolved to produce the modulation parameters $\gamma$ and $\beta .$ Unlike prior conditional normalization methods, $\gamma$ and $\mathbf{\beta}$ are not vectors, but tensors with spatial dimensions. The produced $\gamma$ and $\mathbf{\beta}$ are multiplied and added to the normalized activation element-wise.

Source	AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com